# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-10-03 08:37:07
- **Session ID**: `494575ab-30c7-4b6c-9b2f-04035008eb21`

## Conversation

### 👤 You

Analyze the training dynamics deeply and show me all of the metrics. Note: there is validation results from steps 0, 5 and 10.

wandb: Detected [openai] in use.
wandb: Use W&B Weave for improved LLM call tracing. Weave is installed but not imported. Add `import weave` to the top of your script.
wandb: For more information, check out the docs at: https://weave-docs.wandb.ai/
Checkpoint tracker file does not exist: /root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/latest_checkpointed_iteration.txt
Training from scratch
test_gen_batch meta info: {'eos_token_id': 151645, 'pad_token_id': 151643, 'recompute_log_prob': False, 'do_sample': False, 'validate': True, 'global_steps': 0}
validation generation end
len reward_extra_infos_dict['reward']: 900
len reward_extra_infos_dict['score']: 900
len reward_extra_infos_dict['acc']: 900
len reward_extra_infos_dict['pred']: 900
("Initial validation metrics: {'val-aux/aime_2025/reward/mean@30': "
 "np.float64(-0.3851111111376021), 'val-aux/aime_2025/reward/std@30': "
 "np.float64(0.42077167738958193), 'val-aux/aime_2025/reward/best@2/mean': "
 "np.float64(-0.20551333685715995), 'val-aux/aime_2025/reward/best@2/std': "
 "np.float64(0.4048846267345335), 'val-aux/aime_2025/reward/worst@2/mean': "
 "np.float64(-0.5644566632509231), 'val-aux/aime_2025/reward/worst@2/std': "
 "np.float64(0.2957565382112576), 'val-aux/aime_2025/reward/maj@2/mean': "
 "np.float64(-0.3840500000456969), 'val-aux/aime_2025/reward/maj@2/std': "
 "np.float64(0.42025835719274246), 'val-aux/aime_2025/reward/best@4/mean': "
 "np.float64(-0.03836000666220983), 'val-aux/aime_2025/reward/best@4/std': "
 "np.float64(0.36631685757838633), 'val-aux/aime_2025/reward/worst@4/mean': "
 "np.float64(-0.6927466611405214), 'val-aux/aime_2025/reward/worst@4/std': "
 "np.float64(0.204828436759209), 'val-aux/aime_2025/reward/maj@4/mean': "
 "np.float64(-0.32390000036160155), 'val-aux/aime_2025/reward/maj@4/std': "
 "np.float64(0.36354380873975484), 'val-aux/aime_2025/reward/best@8/mean': "
 "np.float64(0.1228699919641018), 'val-aux/aime_2025/reward/best@8/std': "
 "np.float64(0.3227907348904719), 'val-aux/aime_2025/reward/worst@8/mean': "
 "np.float64(-0.7884866608619691), 'val-aux/aime_2025/reward/worst@8/std': "
 "np.float64(0.13936395091820536), 'val-aux/aime_2025/reward/maj@8/mean': "
 "np.float64(-0.281483334171772), 'val-aux/aime_2025/reward/maj@8/std': "
 "np.float64(0.3130385658322305), 'val-aux/aime_2025/reward/best@16/mean': "
 "np.float64(0.26434999212423965), 'val-aux/aime_2025/reward/best@16/std': "
 "np.float64(0.2491132517973349), 'val-aux/aime_2025/reward/worst@16/mean': "
 "np.float64(-0.8460799946069717), 'val-aux/aime_2025/reward/worst@16/std': "
 "np.float64(0.07383175801399783), 'val-aux/aime_2025/reward/maj@16/mean': "
 "np.float64(-0.24571000123023987), 'val-aux/aime_2025/reward/maj@16/std': "
 "np.float64(0.2863346972491628), 'val-aux/aime_2025/reward/best@30/mean': "
 "np.float64(0.35785332593917846), 'val-aux/aime_2025/reward/best@30/std': "
 "np.float64(0.1759635361986954), 'val-aux/aime_2025/reward/worst@30/mean': "
 "np.float64(-0.8702066616793473), 'val-aux/aime_2025/reward/worst@30/std': "
 "np.float64(0.028394744263279034), 'val-aux/aime_2025/reward/maj@30/mean': "
 "np.float64(-0.21906000173290568), 'val-aux/aime_2025/reward/maj@30/std': "
 "np.float64(0.26707411341892107), 'val-aux/aime_2025/score/mean@30': "
 "np.float64(-0.385111111111111), 'val-aux/aime_2025/score/std@30': "
 "np.float64(0.42077168120087516), 'val-aux/aime_2025/score/best@2/mean': "
 "np.float64(-0.20551333333333333), 'val-aux/aime_2025/score/best@2/std': "
 "np.float64(0.40488462944716697), 'val-aux/aime_2025/score/worst@2/mean': "
 "np.float64(-0.5644566666666667), 'val-aux/aime_2025/score/worst@2/std': "
 "np.float64(0.2957565413649309), 'val-aux/aime_2025/score/maj@2/mean': "
 "np.float64(-0.38405), 'val-aux/aime_2025/score/maj@2/std': "
 "np.float64(0.42025836119888577), 'val-aux/aime_2025/score/best@4/mean': "
 "np.float64(-0.03835999999999997), 'val-aux/aime_2025/score/best@4/std': "
 "np.float64(0.3663168582136505), 'val-aux/aime_2025/score/worst@4/mean': "
 "np.float64(-0.6927466666666667), 'val-aux/aime_2025/score/worst@4/std': "
 "np.float64(0.20482843700638834), 'val-aux/aime_2025/score/maj@4/mean': "
 "np.float64(-0.3239000000000001), 'val-aux/aime_2025/score/maj@4/std': "
 "np.float64(0.36354381292400856), 'val-aux/aime_2025/score/best@8/mean': "
 "np.float64(0.12287000000000003), 'val-aux/aime_2025/score/best@8/std': "
 "np.float64(0.32279073363406396), 'val-aux/aime_2025/score/worst@8/mean': "
 "np.float64(-0.7884866666666667), 'val-aux/aime_2025/score/worst@8/std': "
 "np.float64(0.13936394868113838), 'val-aux/aime_2025/score/maj@8/mean': "
 "np.float64(-0.2814833333333333), 'val-aux/aime_2025/score/maj@8/std': "
 "np.float64(0.31303857028273047), 'val-aux/aime_2025/score/best@16/mean': "
 "np.float64(0.26435000000000003), 'val-aux/aime_2025/score/best@16/std': "
 "np.float64(0.24911325012333352), 'val-aux/aime_2025/score/worst@16/mean': "
 "np.float64(-0.84608), 'val-aux/aime_2025/score/worst@16/std': "
 "np.float64(0.07383175625140394), 'val-aux/aime_2025/score/maj@16/mean': "
 "np.float64(-0.24570999999999998), 'val-aux/aime_2025/score/maj@16/std': "
 "np.float64(0.28633470185138776), 'val-aux/aime_2025/score/best@30/mean': "
 "np.float64(0.3578533333333334), 'val-aux/aime_2025/score/best@30/std': "
 "np.float64(0.17596353507267315), 'val-aux/aime_2025/score/worst@30/mean': "
 "np.float64(-0.8702066666666668), 'val-aux/aime_2025/score/worst@30/std': "
 "np.float64(0.028394743191306145), 'val-aux/aime_2025/score/maj@30/mean': "
 "np.float64(-0.21905999999999998), 'val-aux/aime_2025/score/maj@30/std': "
 "np.float64(0.2670741181705487), 'val-core/aime_2025/acc/mean@30': "
 "np.float64(0.23), 'val-aux/aime_2025/acc/std@30': "
 "np.float64(0.2112859523124106), 'val-aux/aime_2025/acc/best@2/mean': "
 "np.float64(0.3077333333333333), 'val-aux/aime_2025/acc/best@2/std': "
 "np.float64(0.21155051928266674), 'val-aux/aime_2025/acc/worst@2/mean': "
 "np.float64(0.15216666666666664), 'val-aux/aime_2025/acc/worst@2/std': "
 "np.float64(0.13960954180471588), 'val-aux/aime_2025/acc/maj@2/mean': "
 "np.float64(0.2301), 'val-aux/aime_2025/acc/maj@2/std': "
 "np.float64(0.2107474817834632), 'val-aux/aime_2025/acc/best@4/mean': "
 "np.float64(0.38489999999999996), 'val-aux/aime_2025/acc/best@4/std': "
 "np.float64(0.20031865553575467), 'val-aux/aime_2025/acc/worst@4/mean': "
 "np.float64(0.1005), 'val-aux/aime_2025/acc/worst@4/std': "
 "np.float64(0.08995728741283501), 'val-aux/aime_2025/acc/maj@4/mean': "
 "np.float64(0.2669666666666667), 'val-aux/aime_2025/acc/maj@4/std': "
 "np.float64(0.17712979318629582), 'val-aux/aime_2025/acc/best@8/mean': "
 "np.float64(0.4680666666666667), 'val-aux/aime_2025/acc/best@8/std': "
 "np.float64(0.185066533295402), 'val-aux/aime_2025/acc/worst@8/mean': "
 "np.float64(0.06120000000000001), 'val-aux/aime_2025/acc/worst@8/std': "
 "np.float64(0.060217654967566446), 'val-aux/aime_2025/acc/maj@8/mean': "
 "np.float64(0.2922666666666667), 'val-aux/aime_2025/acc/maj@8/std': "
 "np.float64(0.14575728191102494), 'val-aux/aime_2025/acc/best@16/mean': "
 "np.float64(0.5474), 'val-aux/aime_2025/acc/best@16/std': "
 "np.float64(0.14811198042435764), 'val-aux/aime_2025/acc/worst@16/mean': "
 "np.float64(0.039900000000000005), 'val-aux/aime_2025/acc/worst@16/std': "
 "np.float64(0.028000910731110953), 'val-aux/aime_2025/acc/maj@16/mean': "
 "np.float64(0.3127666666666667), 'val-aux/aime_2025/acc/maj@16/std': "
 "np.float64(0.1270609638245232), 'val-core/aime_2025/acc/best@30/mean': "
 "np.float64(0.6026333333333334), 'val-core/aime_2025/acc/best@30/std': "
 "np.float64(0.10652500978873307), 'val-aux/aime_2025/acc/worst@30/mean': "
 "np.float64(0.03363333333333333), 'val-aux/aime_2025/acc/worst@30/std': "
 "np.float64(0.005416186227170555), 'val-core/aime_2025/acc/maj@30/mean': "
 "np.float64(0.3273333333333333), 'val-core/aime_2025/acc/maj@30/std': "
 "np.float64(0.1146489799494343), 'val-aux/num_turns/min': np.int32(2), "
 "'val-aux/num_turns/max': np.int32(16), 'val-aux/num_turns/mean': "
 'np.float64(6.797777777777778)}')
step:0 - val-aux/aime_2025/reward/mean@30:np.float64(-0.3851111111376021) - val-aux/aime_2025/reward/std@30:np.float64(0.42077167738958193) - val-aux/aime_2025/reward/best@2/mean:np.float64(-0.20551333685715995) - val-aux/aime_2025/reward/best@2/std:np.float64(0.4048846267345335) - val-aux/aime_2025/reward/worst@2/mean:np.float64(-0.5644566632509231) - val-aux/aime_2025/reward/worst@2/std:np.float64(0.2957565382112576) - val-aux/aime_2025/reward/maj@2/mean:np.float64(-0.3840500000456969) - val-aux/aime_2025/reward/maj@2/std:np.float64(0.42025835719274246) - val-aux/aime_2025/reward/best@4/mean:np.float64(-0.03836000666220983) - val-aux/aime_2025/reward/best@4/std:np.float64(0.36631685757838633) - val-aux/aime_2025/reward/worst@4/mean:np.float64(-0.6927466611405214) - val-aux/aime_2025/reward/worst@4/std:np.float64(0.204828436759209) - val-aux/aime_2025/reward/maj@4/mean:np.float64(-0.32390000036160155) - val-aux/aime_2025/reward/maj@4/std:np.float64(0.36354380873975484) - val-aux/aime_2025/reward/best@8/mean:np.float64(0.1228699919641018) - val-aux/aime_2025/reward/best@8/std:np.float64(0.3227907348904719) - val-aux/aime_2025/reward/worst@8/mean:np.float64(-0.7884866608619691) - val-aux/aime_2025/reward/worst@8/std:np.float64(0.13936395091820536) - val-aux/aime_2025/reward/maj@8/mean:np.float64(-0.281483334171772) - val-aux/aime_2025/reward/maj@8/std:np.float64(0.3130385658322305) - val-aux/aime_2025/reward/best@16/mean:np.float64(0.26434999212423965) - val-aux/aime_2025/reward/best@16/std:np.float64(0.2491132517973349) - val-aux/aime_2025/reward/worst@16/mean:np.float64(-0.8460799946069717) - val-aux/aime_2025/reward/worst@16/std:np.float64(0.07383175801399783) - val-aux/aime_2025/reward/maj@16/mean:np.float64(-0.24571000123023987) - val-aux/aime_2025/reward/maj@16/std:np.float64(0.2863346972491628) - val-aux/aime_2025/reward/best@30/mean:np.float64(0.35785332593917846) - val-aux/aime_2025/reward/best@30/std:np.float64(0.1759635361986954) - val-aux/aime_2025/reward/worst@30/mean:np.float64(-0.8702066616793473) - val-aux/aime_2025/reward/worst@30/std:np.float64(0.028394744263279034) - val-aux/aime_2025/reward/maj@30/mean:np.float64(-0.21906000173290568) - val-aux/aime_2025/reward/maj@30/std:np.float64(0.26707411341892107) - val-aux/aime_2025/score/mean@30:np.float64(-0.385111111111111) - val-aux/aime_2025/score/std@30:np.float64(0.42077168120087516) - val-aux/aime_2025/score/best@2/mean:np.float64(-0.20551333333333333) - val-aux/aime_2025/score/best@2/std:np.float64(0.40488462944716697) - val-aux/aime_2025/score/worst@2/mean:np.float64(-0.5644566666666667) - val-aux/aime_2025/score/worst@2/std:np.float64(0.2957565413649309) - val-aux/aime_2025/score/maj@2/mean:np.float64(-0.38405) - val-aux/aime_2025/score/maj@2/std:np.float64(0.42025836119888577) - val-aux/aime_2025/score/best@4/mean:np.float64(-0.03835999999999997) - val-aux/aime_2025/score/best@4/std:np.float64(0.3663168582136505) - val-aux/aime_2025/score/worst@4/mean:np.float64(-0.6927466666666667) - val-aux/aime_2025/score/worst@4/std:np.float64(0.20482843700638834) - val-aux/aime_2025/score/maj@4/mean:np.float64(-0.3239000000000001) - val-aux/aime_2025/score/maj@4/std:np.float64(0.36354381292400856) - val-aux/aime_2025/score/best@8/mean:np.float64(0.12287000000000003) - val-aux/aime_2025/score/best@8/std:np.float64(0.32279073363406396) - val-aux/aime_2025/score/worst@8/mean:np.float64(-0.7884866666666667) - val-aux/aime_2025/score/worst@8/std:np.float64(0.13936394868113838) - val-aux/aime_2025/score/maj@8/mean:np.float64(-0.2814833333333333) - val-aux/aime_2025/score/maj@8/std:np.float64(0.31303857028273047) - val-aux/aime_2025/score/best@16/mean:np.float64(0.26435000000000003) - val-aux/aime_2025/score/best@16/std:np.float64(0.24911325012333352) - val-aux/aime_2025/score/worst@16/mean:np.float64(-0.84608) - val-aux/aime_2025/score/worst@16/std:np.float64(0.07383175625140394) - val-aux/aime_2025/score/maj@16/mean:np.float64(-0.24570999999999998) - val-aux/aime_2025/score/maj@16/std:np.float64(0.28633470185138776) - val-aux/aime_2025/score/best@30/mean:np.float64(0
Training Progress:   0%|▌                                                                                                                                                | 14/3499 [20:35:11<5552:33:53, 5735.79s/it]
step:1 - global_seqlen/min:2617603 - global_seqlen/max:3287075 - global_seqlen/minmax_diff:669472 - global_seqlen/balanced_min:2935937 - global_seqlen/balanced_max:2935938 - global_seqlen/mean:2935937.125 - actor/entropy:0.15639255940914154 - actor/pg_loss:np.float64(0.00021197963747567882) - actor/pg_clipfrac:np.float64(0.0019516451851814055) - actor/ppo_kl:np.float64(2.393998338045359e-05) - actor/pg_clipfrac_lower:np.float64(2.5442191144975366e-07) - actor/grad_norm:np.float64(0.15237574465572834) - perf/mfu/actor:np.float64(0.3851863981657599) - perf/max_memory_allocated_gb:np.float64(204.84540128707886) - perf/max_memory_reserved_gb:np.float64(216.712890625) - perf/cpu_memory_used_gb:np.float64(173.94210815429688) - actor/lr:np.float64(1e-06) - training/global_step:1 - training/epoch:0 - critic/score/mean:0.04688720405101776 - critic/score/max:1.0 - critic/score/min:-1.0 - critic/rewards/mean:0.04688720405101776 - critic/rewards/max:1.0 - critic/rewards/min:-1.0 - critic/advantages/mean:-0.005949620623141527 - critic/advantages/max:3.749990463256836 - critic/advantages/min:-3.7499921321868896 - critic/returns/mean:-0.005949620623141527 - critic/returns/max:3.749990463256836 - critic/returns/min:-3.7499921321868896 - response_length/mean:2535.5634765625 - response_length/max:10240.0 - response_length/min:240.0 - response_length/clip_ratio:0.0252685546875 - response_length_non_aborted/mean:2535.5634765625 - response_length_non_aborted/max:10240.0 - response_length_non_aborted/min:240.0 - response_length_non_aborted/clip_ratio:0.0252685546875 - response/aborted_ratio:0.0 - prompt_length/mean:331.5625 - prompt_length/max:787.0 - prompt_length/min:256.0 - prompt_length/clip_ratio:0.0 - num_turns/min:np.int32(2) - num_turns/max:np.int32(16) - num_turns/mean:np.float64(7.819091796875) - timing_s/start_profile:0.00024098530411720276 - timing_s/agent_loop/generate_sequences/min:np.float64(23.217133008874953) - timing_s/agent_loop/generate_sequences/max:np.float64(2109.645097628236) - timing_s/agent_loop/generate_sequences/mean:np.float64(1300.944870188906) - timing_s/agent_loop/tool_calls/min:np.float64(0.0) - timing_s/agent_loop/tool_calls/max:np.float64(61.0761140929535) - timing_s/agent_loop/tool_calls/mean:np.float64(0.40351148001889214) - timing_s/agent_loop/slowest/generate_sequences:np.float64(2109.645097628236) - timing_s/agent_loop/slowest/tool_calls:np.float64(0.26344546396285295) - timing_s/agent_loop/slowest/prompt_length:297 - timing_s/agent_loop/slowest/response_length:10240 - timing_s/gen:2143.2214297391474 - timing_s/reward:0.00014317315071821213 - timing_s/old_log_prob:480.196880501695 - timing_s/adv:0.7246750853955746 - timing_s/update_actor:1707.9158686129376 - timing_s/step:4334.193368503824 - timing_s/stop_profile:0.00012270640581846237 - timing_per_token_ms/adv:3.0853654446415853e-05 - timing_per_token_ms/update_actor:0.07271596208522771 - timing_per_token_ms/gen:0.10318168411214124 - perf/total_num_tokens:23487497 - perf/time_per_step:4334.193368503824 - perf/throughput:677.3895106607794
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test_gen_batch meta info: {'eos_token_id': 151645, 'pad_token_id': 151643, 'recompute_log_prob': False, 'do_sample': False, 'validate': True, 'global_steps': 5}
validation generation end
len reward_extra_infos_dict['reward']: 900
len reward_extra_infos_dict['score']: 900
len reward_extra_infos_dict['acc']: 900
len reward_extra_infos_dict['pred']: 900
local_global_step_folder: /root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/global_step_5
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test_gen_batch meta info: {'eos_token_id': 151645, 'pad_token_id': 151643, 'recompute_log_prob': False, 'do_sample': False, 'validate': True, 'global_steps': 10}
validation generation end
len reward_extra_infos_dict['reward']: 900
len reward_extra_infos_dict['score']: 900
len reward_extra_infos_dict['acc']: 900
len reward_extra_infos_dict['pred']: 900
local_global_step_folder: /root/verl/recipe/retool/checkpoint/qwen2.5-32b_dapo_with_tool/global_step_10
step:10 - global_seqlen/min:3300182 - global_seqlen/max:3754380 - global_seqlen/minmax_diff:454198 - global_seqlen/balanced_min:3533622 - global_seqlen/balanced_max:3533623 - global_seqlen/mean:3533622.625 - actor/entropy:0.17678989470005035 - actor/pg_loss:np.float64(0.000596857187390122) - actor/pg_clipfrac:np.float64(0.0015710217719768277) - actor/ppo_kl:np.float64(2.2217085910559832e-05) - actor/pg_clipfrac_lower:np.float64(8.161514289086982e-08) - actor/grad_norm:np.float64(0.11105571500957012) - perf/mfu/actor:np.float64(0.3899108360673109) - perf/max_memory_allocated_gb:np.float64(204.91833114624023) - perf/max_memory_reserved_gb:np.float64(216.771484375) - perf/cpu_memory_used_gb:np.float64(183.14881134033203) - actor/lr:np.float64(1e-06) - val-aux/aime_2025/reward/mean@30:np.float64(-0.3027777782413695) - val-aux/aime_2025/reward/std@30:np.float64(0.5030693212772173) - val-aux/aime_2025/reward/best@2/mean:np.float64(-0.0861133365492026) - val-aux/aime_2025/reward/best@2/std:np.float64(0.4510919775204411) - val-aux/aime_2025/reward/worst@2/mean:np.float64(-0.511989997702837) - val-aux/aime_2025/reward/worst@2/std:np.float64(0.38100752314899716) - val-aux/aime_2025/reward/maj@2/mean:np.float64(-0.2962933338483174) - val-aux/aime_2025/reward/maj@2/std:np.float64(0.4964716872781644) - val-aux/aime_2025/reward/best@4/mean:np.float64(0.10289999426603316) - val-aux/aime_2025/reward/best@4/std:np.float64(0.39895776327496907) - val-aux/aime_2025/reward/worst@4/mean:np.float64(-0.6700966624855995) - val-aux/aime_2025/reward/worst@4/std:np.float64(0.28268466486088584) - val-aux/aime_2025/reward/maj@4/mean:np.float64(-0.23634333412448563) - val-aux/aime_2025/reward/maj@4/std:np.float64(0.41077975649976156) - val-aux/aime_2025/reward/best@8/mean:np.float64(0.27334332636992137) - val-aux/aime_2025/reward/best@8/std:np.float64(0.3354740544532477) - val-aux/aime_2025/reward/worst@8/mean:np.float64(-0.7938399950583775) - val-aux/aime_2025/reward/worst@8/std:np.float64(0.20211893037848702) - val-aux/aime_2025/reward/maj@8/mean:np.float64(-0.2088433345079422) - val-aux/aime_2025/reward/maj@8/std:np.float64(0.31888629830546306) - val-aux/aime_2025/reward/best@16/mean:np.float64(0.42198332631985347) - val-aux/aime_2025/reward/best@16/std:np.float64(0.260134819962231) - val-aux/aime_2025/reward/worst@16/mean:np.float64(-0.8790566614369553) - val-aux/aime_2025/reward/worst@16/std:np.float64(0.12865832024918072) - val-aux/aime_2025/reward/maj@16/mean:np.float64(-0.2003866682032744) - val-aux/aime_2025/reward/maj@16/std:np.float64(0.24245827855974153) - val-aux/aime_2025/reward/best@30/mean:np.float64(0.5211266600807508) - val-aux/aime_2025/reward/best@30/std:np.float64(0.1820060683974367) - val-aux/aime_2025/reward/worst@30/mean:np.float64(-0.9267499950329462) - val-aux/aime_2025/reward/worst@30/std:np.float64(0.07543149322241265) - val-aux/aime_2025/reward/maj@30/mean:np.float64(-0.19839666853944463) - val-aux/aime_2025/reward/maj@30/std:np.float64(0.18848508741758838) - val-aux/aime_2025/score/mean@30:np.float64(-0.3027777777777778) - val-aux/aime_2025/score/std@30:np.float64(0.5030693243927806) - val-aux/aime_2025/score/best@2/mean:np.float64(-0.08611333333333332) - val-aux/aime_2025/score/best@2/std:np.float64(0.4510919795274291) - val-aux/aime_2025/score/worst@2/mean:np.float64(-0.5119900000000001) - val-aux/aime_2025/score/worst@2/std:np.float64(0.3810075260549735) - val-aux/aime_2025/score/maj@2/mean:np.float64(-0.2962933333333334) - val-aux/aime_2025/score/maj@2/std:np.float64(0.49647169040158823) - val-aux/aime_2025/score/best@4/mean:np.float64(0.10290000000000002) - val-aux/aime_2025/score/best@4/std:np.float64(0.3989577636469225) - val-aux/aime_2025/score/worst@4/mean:np.float64(-0.6700966666666669) - val-aux/aime_2025/score/worst@4/std:np.float64(0.2826846654309934) - val-aux/aime_2025/score/maj@4/mean:np.float64(-0.2363433333333333) - val-aux/aime_2025/score/maj@4/std:np.float64(0.41077975964189956) - val-aux/aime_2025/score/best@8/mean:np.float64(0.2733433333333333) - val-aux/aime_2025/score/best@8/std:np.float64(0.335474
step:11 - global_seqlen/min:3501508 - global_seqlen/max:3941969 - global_seqlen/minmax_diff:440461 - global_seqlen/balanced_min:3609380 - global_seqlen/balanced_max:3609381 - global_seqlen/mean:3609380.875 - actor/entropy:0.17943327128887177 - actor/pg_loss:np.float64(0.0009584601506998223) - actor/pg_clipfrac:np.float64(0.0014703691413160414) - actor/ppo_kl:np.float64(1.1130273748762733e-05) - actor/pg_clipfrac_lower:np.float64(3.993071963172091e-08) - actor/grad_norm:np.float64(0.11253686435520649) - perf/mfu/actor:np.float64(0.38858967808036393) - perf/max_memory_allocated_gb:np.float64(204.91833114624023) - perf/max_memory_reserved_gb:np.float64(216.771484375) - perf/cpu_memory_used_gb:np.float64(186.63153457641602) - actor/lr:np.float64(1e-06) - training/global_step:11 - training/epoch:0 - critic/score/mean:0.21043701469898224 - critic/score/max:1.0 - critic/score/min:-1.0 - critic/rewards/mean:0.21043701469898224 - critic/rewards/max:1.0 - critic/rewards/min:-1.0 - critic/advantages/mean:-0.03543281927704811 - critic/advantages/max:3.7106359004974365 - critic/advantages/min:-3.7499923706054688 - critic/returns/mean:-0.03543281927704811 - critic/returns/max:3.7106359004974365 - critic/returns/min:-3.7499923706054688 - response_length/mean:3188.3525390625 - response_length/max:10240.0 - response_length/min:220.0 - response_length/clip_ratio:0.0416259765625 - response_length_non_aborted/mean:3188.3525390625 - response_length_non_aborted/max:10240.0 - response_length_non_aborted/min:220.0 - response_length_non_aborted/clip_ratio:0.0416259765625 - response/aborted_ratio:0.0 - prompt_length/mean:336.43359375 - prompt_length/max:936.0 - prompt_length/min:259.0 - prompt_length/clip_ratio:0.0 - num_turns/min:np.int32(2) - num_turns/max:np.int32(16) - num_turns/mean:np.float64(7.3681640625) - timing_s/start_profile:0.00011372938752174377 - timing_s/agent_loop/generate_sequences/min:np.float64(35.530964098870754) - timing_s/agent_loop/generate_sequences/max:np.float64(2831.5208687549457) - timing_s/agent_loop/generate_sequences/mean:np.float64(1896.8434308441595) - timing_s/agent_loop/tool_calls/min:np.float64(0.0) - timing_s/agent_loop/tool_calls/max:np.float64(90.45875699259341) - timing_s/agent_loop/tool_calls/mean:np.float64(0.45699733924777775) - timing_s/agent_loop/slowest/generate_sequences:np.float64(2831.5208687549457) - timing_s/agent_loop/slowest/tool_calls:np.float64(0.34081609547138214) - timing_s/agent_loop/slowest/prompt_length:334 - timing_s/agent_loop/slowest/response_length:10240 - timing_s/gen:2868.1163079533726 - timing_s/reward:0.010729270055890083 - timing_s/old_log_prob:571.8586511826143 - timing_s/adv:0.6862698970362544 - timing_s/update_actor:2080.960430746898 - timing_s/step:5523.769419168122 - timing_s/stop_profile:0.00011165253818035126 - timing_per_token_ms/adv:2.3766883332497123e-05 - timing_per_token_ms/update_actor:0.07206777390454548 - timing_per_token_ms/gen:0.10980964297667063 - perf/total_num_tokens:28875047 - perf/time_per_step:5523.769419168122 - perf/throughput:653.4271438766124
step:12 - global_seqlen/min:3518728 - global_seqlen/max:3987964 - global_seqlen/minmax_diff:469236 - global_seqlen/balanced_min:3745106 - global_seqlen/balanced_max:3745107 - global_seqlen/mean:3745106.5 - actor/entropy:0.18342816829681396 - actor/pg_loss:np.float64(0.000772155136887821) - actor/pg_clipfrac:np.float64(0.0014049845983838448) - actor/ppo_kl:np.float64(2.502474207388125e-05) - actor/pg_clipfrac_lower:np.float64(1.5146262436852945e-07) - actor/grad_norm:np.float64(0.10397677589207888) - perf/mfu/actor:np.float64(0.38751717573042616) - perf/max_memory_allocated_gb:np.float64(204.91833114624023) - perf/max_memory_reserved_gb:np.float64(216.771484375) - perf/cpu_memory_used_gb:np.float64(185.26648712158203) - actor/lr:np.float64(1e-06) - training/global_step:12 - training/epoch:0 - critic/score/mean:0.2681518793106079 - critic/score/max:1.0 - critic/score/min:-1.0 - critic/rewards/mean:0.2681518793106079 - critic/rewards/max:1.0 - critic/rewards/min:-1.0 - critic/advantages/mean:-0.029992280527949333 - critic/advantages/max:3.72918963432312 - critic/advantages/min:-3.7499923706054688 - critic/returns/mean:-0.029992280527949333 - critic/returns/max:3.72918963432312 - critic/returns/min:-3.7499923706054688 - response_length/mean:3321.871337890625 - response_length/max:10240.0 - response_length/min:52.0 - response_length/clip_ratio:0.04638671875 - response_length_non_aborted/mean:3321.871337890625 - response_length_non_aborted/max:10240.0 - response_length_non_aborted/min:52.0 - response_length_non_aborted/clip_ratio:0.04638671875 - response/aborted_ratio:0.0 - prompt_length/mean:335.458984375 - prompt_length/max:757.0 - prompt_length/min:257.0 - prompt_length/clip_ratio:0.0 - num_turns/min:np.int32(2) - num_turns/max:np.int32(16) - num_turns/mean:np.float64(7.2978515625) - timing_s/start_profile:5.807261914014816e-05 - timing_s/agent_loop/generate_sequences/min:np.float64(39.721646742895246) - timing_s/agent_loop/generate_sequences/max:np.float64(3014.9129755953327) - timing_s/agent_loop/generate_sequences/mean:np.float64(2026.5895920487308) - timing_s/agent_loop/tool_calls/min:np.float64(0.0) - timing_s/agent_loop/tool_calls/max:np.float64(90.32616233546287) - timing_s/agent_loop/tool_calls/mean:np.float64(0.46768241583856707) - timing_s/agent_loop/slowest/generate_sequences:np.float64(3014.9129755953327) - timing_s/agent_loop/slowest/tool_calls:np.float64(0.851713097654283) - timing_s/agent_loop/slowest/prompt_length:369 - timing_s/agent_loop/slowest/response_length:10240 - timing_s/gen:3056.6701939683408 - timing_s/reward:0.014445105567574501 - timing_s/old_log_prob:593.8498902060091 - timing_s/adv:0.6515557365491986 - timing_s/update_actor:2170.301889812574 - timing_s/step:5823.763349493034 - timing_s/stop_profile:0.00012017879635095596 - timing_per_token_ms/adv:2.1746904261701476e-05 - timing_per_token_ms/update_actor:0.07243792782289467 - timing_per_token_ms/gen:0.11232484579733489 - perf/total_num_tokens:29960852 - perf/time_per_step:5823.763349493034 - perf/throughput:643.0732629831217
step:13 - global_seqlen/min:3509058 - global_seqlen/max:3979752 - global_seqlen/minmax_diff:470694 - global_seqlen/balanced_min:3709813 - global_seqlen/balanced_max:3709814 - global_seqlen/mean:3709813.75 - actor/entropy:0.18159879744052887 - actor/pg_loss:np.float64(0.0005575024349131603) - actor/pg_clipfrac:np.float64(0.0014169658959297905) - actor/ppo_kl:np.float64(5.0915489346063e-06) - actor/pg_clipfrac_lower:np.float64(1.149224939772945e-07) - actor/grad_norm:np.float64(0.10177393164485693) - perf/mfu/actor:np.float64(0.3884225886366707) - perf/max_memory_allocated_gb:np.float64(204.91833114624023) - perf/max_memory_reserved_gb:np.float64(216.771484375) - perf/cpu_memory_used_gb:np.float64(185.39531326293945) - actor/lr:np.float64(1e-06) - training/global_step:13 - training/epoch:0 - critic/score/mean:0.28886717557907104 - critic/score/max:1.0 - critic/score/min:-1.0 - critic/rewards/mean:0.28886717557907104 - critic/rewards/max:1.0 - critic/rewards/min:-1.0 - critic/advantages/mean:-0.0216106865555048 - critic/advantages/max:3.700279712677002 - critic/advantages/min:-3.7499923706054688 - critic/returns/mean:-0.0216106865555048 - critic/returns/max:3.700279712677002 - critic/returns/min:-3.7499923706054688 - response_length/mean:3287.179443359375 - response_length/max:10240.0 - response_length/min:316.0 - response_length/clip_ratio:0.040283203125 - response_length_non_aborted/mean:3287.179443359375 - response_length_non_aborted/max:10240.0 - response_length_non_aborted/min:316.0 - response_length_non_aborted/clip_ratio:0.040283203125 - response/aborted_ratio:0.0 - prompt_length/mean:335.685546875 - prompt_length/max:879.0 - prompt_length/min:255.0 - prompt_length/clip_ratio:0.0 - num_turns/min:np.int32(2) - num_turns/max:np.int32(16) - num_turns/mean:np.float64(7.27734375) - timing_s/start_profile:4.798267036676407e-05 - timing_s/agent_loop/generate_sequences/min:np.float64(26.443047213368118) - timing_s/agent_loop/generate_sequences/max:np.float64(2915.121200089343) - timing_s/agent_loop/generate_sequences/mean:np.float64(1959.6631171195438) - timing_s/agent_loop/tool_calls/min:np.float64(0.0) - timing_s/agent_loop/tool_calls/max:np.float64(91.49014924839139) - timing_s/agent_loop/tool_calls/mean:np.float64(0.513923424402492) - timing_s/agent_loop/slowest/generate_sequences:np.float64(2915.121200089343) - timing_s/agent_loop/slowest/tool_calls:np.float64(0.32805391028523445) - timing_s/agent_loop/slowest/prompt_length:373 - timing_s/agent_loop/slowest/response_length:10240 - timing_s/gen:2955.5168409002945 - timing_s/reward:0.015270061790943146 - timing_s/old_log_prob:590.1571235880256 - timing_s/adv:0.6097756884992123 - timing_s/update_actor:2140.665639639832 - timing_s/step:5689.172893516719 - timing_s/stop_profile:3.125239163637161e-05 - timing_per_token_ms/adv:2.0546034437012245e-05 - timing_per_token_ms/update_actor:0.07212847409252797 - timing_per_token_ms/gen:0.10975393056090882 - perf/total_num_tokens:29678510 - perf/time_per_step:5689.172893516719 - perf/throughput:652.0831444984979
step:14 - global_seqlen/min:3563127 - global_seqlen/max:4047226 - global_seqlen/minmax_diff:484099 - global_seqlen/balanced_min:3818685 - global_seqlen/balanced_max:3818685 - global_seqlen/mean:3818685.0 - actor/entropy:0.1878919005393982 - actor/pg_loss:np.float64(0.0006540682723050978) - actor/pg_clipfrac:np.float64(0.0013270985806369795) - actor/ppo_kl:np.float64(1.978388105346067e-05) - actor/pg_clipfrac_lower:np.float64(0.0) - actor/grad_norm:np.float64(0.1036995118483901) - perf/mfu/actor:np.float64(0.38890370907286975) - perf/max_memory_allocated_gb:np.float64(204.91833114624023) - perf/max_memory_reserved_gb:np.float64(216.771484375) - perf/cpu_memory_used_gb:np.float64(186.3760223388672) - actor/lr:np.float64(1e-06) - training/global_step:14 - training/epoch:0 - critic/score/mean:0.28533935546875 - critic/score/max:1.0 - critic/score/min:-1.0 - critic/rewards/mean:0.28533935546875 - critic/rewards/max:1.0 - critic/rewards/min:-1.0 - critic/advantages/mean:-0.026025746017694473 - critic/advantages/max:3.7280261516571045 - critic/advantages/min:-3.7499923706054688 - critic/returns/mean:-0.026025746017694473 - critic/returns/max:3.7280261516571045 - critic/returns/min:-3.7499923706054688 - response_length/mean:3396.9111328125 - response_length/max:10240.0 - response_length/min:321.0 - response_length/clip_ratio:0.04443359375 - response_length_non_aborted/mean:3396.9111328125 - response_length_non_aborted/max:10240.0 - response_length_non_aborted/min:321.0 - response_length_non_aborted/clip_ratio:0.04443359375 - response/aborted_ratio:0.0 - prompt_length/mean:332.2734375 - prompt_length/max:772.0 - prompt_length/min:254.0 - prompt_length/clip_ratio:0.0 - num_turns/min:np.int32(2) - num_turns/max:np.int32(16) - num_turns/mean:np.float64(7.128173828125) - timing_s/start_profile:5.393289029598236e-05 - timing_s/agent_loop/generate_sequences/min:np.float64(57.030645839869976) - timing_s/agent_loop/generate_sequences/max:np.float64(3059.043283865787) - timing_s/agent_loop/generate_sequences/mean:np.float64(2083.610888967869) - timing_s/agent_loop/tool_calls/min:np.float64(0.0) - timing_s/agent_loop/tool_calls/max:np.float64(60.98349663708359) - timing_s/agent_loop/tool_calls/mean:np.float64(0.49042526011373866) - timing_s/agent_loop/slowest/generate_sequences:np.float64(3059.02440092247) - timing_s/agent_loop/slowest/tool_calls:np.float64(0.6667332593351603) - timing_s/agent_loop/slowest/prompt_length:319 - timing_s/agent_loop/slowest/response_length:10240 - timing_s/gen:3100.501138087362 - timing_s/reward:0.01253920141607523 - timing_s/old_log_prob:606.2569427117705 - timing_s/adv:0.6319898953661323 - timing_s/update_actor:2203.01897084713 - timing_s/step:5912.553314724006 - timing_s/stop_profile:0.00011175405234098434 - timing_per_token_ms/adv:2.0687419077710397e-05 - timing_per_token_ms/update_actor:0.07211314139707549 - timing_per_token_ms/gen:0.11141861768976132 - perf/total_num_tokens:30549480 - perf/time_per_step:5912.553314724006 - perf/throughput:645.8605608663765

---

### 🤖 Cursor Assistant

I'll analyze the training dynamics comprehensively by extracting and visualizing all the key metrics from your training log.

---

### 🤖 Cursor Assistant

Now let me create visualization scripts to plot these metrics:

---

### 👤 You

(TaskRunner pid=835480) step:15 - global_seqlen/min:3621458 - global_seqlen/max:4323973 - global_seqlen/minmax_diff:702515 - global_seqlen/balanced_min:3992675 - global_seqlen/balanced_max:3992676 - global_seqlen/m
ean:3992675.875 - actor/entropy:0.18974125385284424 - actor/pg_loss:np.float64(0.00111400772929926) - actor/pg_clipfrac:np.float64(0.0013660089208293001) - actor/ppo_kl:np.float64(4.103280344879306e-06) - actor/pg_
clipfrac_lower:np.float64(3.5310516223700424e-08) - actor/grad_norm:np.float64(0.10015907045453787) - perf/mfu/actor:np.float64(0.3891635434554998) - perf/max_memory_allocated_gb:np.float64(204.91833114624023) - pe
rf/max_memory_reserved_gb:np.float64(216.771484375) - perf/cpu_memory_used_gb:np.float64(187.5531349182129) - actor/lr:np.float64(1e-06) - val-aux/aime_2025/reward/mean@30:np.float64(-0.3222222218910853) - val-aux/
aime_2025/reward/std@30:np.float64(0.4512588870405507) - val-aux/aime_2025/reward/best@2/mean:np.float64(-0.13704000203808148) - val-aux/aime_2025/reward/best@2/std:np.float64(0.4233366276045155) - val-aux/aime_202
5/reward/worst@2/mean:np.float64(-0.5083299974739551) - val-aux/aime_2025/reward/worst@2/std:np.float64(0.3185805808091476) - val-aux/aime_2025/reward/maj@2/mean:np.float64(-0.3232499998569489) - val-aux/aime_2025/
reward/maj@2/std:np.float64(0.4471863796289078) - val-aux/aime_2025/reward/best@4/mean:np.float64(0.04545999576648075) - val-aux/aime_2025/reward/best@4/std:np.float64(0.3896704537842796) - val-aux/aime_2025/reward
/worst@4/mean:np.float64(-0.6344466627518337) - val-aux/aime_2025/reward/worst@4/std:np.float64(0.24204328734649694) - val-aux/aime_2025/reward/maj@4/mean:np.float64(-0.2893633337120215) - val-aux/aime_2025/reward/
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Analyze results from step 15. Do not write file. Tell me directly

---

### 🤖 Cursor Assistant

## Step 15 Analysis: **VALIDATION REGRESSION DETECTED** ⚠️

### Critical Finding: Performance Degraded from Step 10 → Step 15

**Validation Metrics Comparison:**

| Metric | Step 10 | Step 15 | Change |
|--------|---------|---------|--------|
| **mean@30 reward** | -0.3028 | **-0.3222** | **-6.4% (WORSE)** ⚠️ |
| **mean@30 accuracy** | N/A | 28.22% | — |
| **best@30 reward** | +0.521 | **+0.449** | **-13.8% (WORSE)** 🚨 |
| **best@30 accuracy** | N/A | 66.46% | (vs 60.3% at step 0) |
| **maj@30 reward** | -0.198 | **-0.270** | **-36.4% (WORSE)** 🚨 |

### All Best@N Metrics Regressed

| Samples | Step 10 | Step 15 | Δ |
|---------|---------|---------|---|
| best@2 | -0.086 | -0.137 | -59% worse |
| best@4 | +0.103 | +0.045 | -56% worse |
| best@8 | +0.273 | +0.218 | -20% worse |
| best@16 | +0.422 | +0.352 | -17% worse |
| best@30 | +0.521 | +0.449 | -14% worse |

**This is a complete reversal** - the model generates fewer correct solutions at validation.

### Training Metrics (Step 15) - Still Improving!

- **Training reward**: 28.95% (continued growth from 28.53% at step 14)
- **Entropy**: 0.1897 (+0.6% from step 14) - still exploring
- **Grad norm**: 0.1002 (-3.4% from step 14) - continuing to decrease
- **PG loss**: 0.0011 (highest since step 9)
- **Response length**: 3,567 tokens (+5% from step 14)
- **Clip ratio**: 5.44% (hitting token limit **more often**)

### Behavioral Changes

**Validation-specific patterns:**
- **Num turns**: 5.83 (validation) vs 7.08 (training)
  - Validation uses **16% fewer turns** than training
  - Step 10 validation used 6.97 turns → **dropped 16%**
- **Shorter reasoning chains** on validation problems

### Root Cause Analysis

This regression indicates **overfitting to the training distribution**:

1. **Train-Val Divergence**: Training reward ↑ (28.95%), validation ↓ (-32.22%)
2. **Behavioral Shift**: Model using fewer turns on validation (5.83 vs 7.08 training)
3. **Token Limit Pressure**: 5.44% responses hitting 10k limit (up 23% from step 14)
4. **Decreasing Gradients**: Grad norm at 0.100, down 32% from step 1

**Hypothesis**: The model learned to generate **longer, more complex reasoning** that works on training examples but **doesn't generalize** to validation problems. The validation problems may require different reasoning patterns.

### Immediate Implications

🚨 **TRAINING IS NOT CONVERGING PROPERLY**

The model is:
- ✅ Memorizing training patterns (reward: 4.7% → 28.9%)
- ❌ NOT improving validation generalization (step 10→15 regression)
- ⚠️ Generating longer responses that don't help validation accuracy

### Recommended Actions

**IMMEDIATE (before step 20)**:
1. **Check step 16-20 validation** - is this a temporary fluctuation or trend?
2. **Analyze training vs validation problem distribution** - are they truly from the same distribution?
3. **Inspect examples** - what are the differences in step 10 vs step 15 solutions?

**SHORT-TERM (if regression continues)**:
1. **Reduce learning rate** from 1e-6 to 5e-7 (slow down overfitting)
2. **Add regularization** - entropy bonus to prevent collapse
3. **Increase validation frequency** - validate every 2-3 steps instead of 5
4. **Early stopping consideration** - step 10 may have been the peak

**MEDIUM-TERM**:
1. **Curriculum adjustment** - training problems may be too different from validation
2. **Response length penalty** - penalize excessive token usage
3. **Diversity regularization** - prevent memorization of specific patterns

### Variance Analysis

**Validation std dev trajectory:**
- Step 0: 0.421
- Step 5: 0.453 (+7.6%)
- Step 10: 0.503 (+11%)
- Step 15: **0.451** (-10.3% from step 10)

**Decreasing variance** alongside decreasing performance suggests the model is becoming **more confident but less correct** - a classic overfitting signal.

### Accuracy Deep Dive

Looking at accuracy bands:

| Metric | Step 0 | Step 15 | Change |
|--------|--------|---------|--------|
| acc/best@30 | 60.26% | 66.46% | +10.3% ✅ |
| acc/maj@30 | 32.73% | 32.78% | +0.2% (flat) |
| acc/worst@30 | 3.36% | 4.83% | +43.8% |

**Interpretation**: The "worst" solutions improved significantly (+44%), and "best" improved (+10%), but **majority vote stagnated**. This means the model generates more diverse attempts but hasn't improved the **reliability** of any single attempt.

### Bottom Line

**Step 15 shows REGRESSION, not progress.** Despite training metrics improving, the model is **overfitting**. The training from step 10→15 may have **degraded** the model's generalization ability.

**Status**: 🚨 **WARNING - Consider reverting to step 10 checkpoint** and adjusting hyperparameters before continuing.

---


*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*