# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-10-01 19:03:09
- **Session ID**: `1bf48f2c-dd2c-4da5-af21-b015d2243ce4`

## Conversation

### 👤 You

actor_rollout_ref.rollout.trace.backend=weave

If I do not specify this param, what backend will I use for trace?

You may check the verl code in the repo

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### 🤖 Cursor Assistant

I'll help you find out what the default trace backend is when `actor_rollout_ref.rollout.trace.backend` is not specified. Let me search through the verl code.

---

### 🤖 Cursor Assistant

Based on the verl code I've examined, here's the answer:

## Default Trace Backend

**If you do not specify `actor_rollout_ref.rollout.trace.backend`, the default value is `None`, which means tracing will be disabled.**

### Evidence from the code:

1. **Configuration definition** (`verl/workers/config/rollout.py:74-77`):
```python
@dataclass
class TraceConfig(BaseConfig):
    backend: Optional[str] = None
    token2text: bool = False
```

2. **Trace implementation** (`verl/utils/rollout_trace.py:37-38`):
```python
class RolloutTraceConfig:
    backend: Optional[str] = None
```

3. **Behavior when backend is None** (`verl/utils/rollout_trace.py:145-146`):
```python
backend = RolloutTraceConfig.get_backend()
if backend is None:
    return await func(self, *args, **kwargs)
```

When the backend is `None`, the `rollout_trace_op` decorator simply executes the function without any tracing instrumentation.

### Available Options

According to the documentation, you have two tracing backends available:
- `weave` - wandb's weave tracing
- `mlflow` - MLflow tracing

If you want to enable tracing, you must explicitly set one of these backends.

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### 👤 You

If rollout trace is enabled, will all the rollouts be saved?

---

### 🤖 Cursor Assistant

Based on my examination of the verl code, I can now answer your question:

## Yes, when rollout trace is enabled, **ALL rollouts will be saved**.

Here's how it works:

### How Tracing Works

1. **Individual Sample Tracing** - In async mode, each sample/trajectory is processed individually with its own trace. Looking at the `AgentLoopWorker.generate_sequences` method:

```473:505:projects/week7/verl/verl/experimental/agent_loop/agent_loop.py
    async def _generate_single_trajectory(
        self,
        sampling_params: dict[str, Any],
        trajectory: dict[str, Any],
        *,
        agent_name: str,
        **kwargs,
    ) -> _InternalAgentLoopOutput:
        with rollout_trace_attr(
            step=trajectory["step"],
            sample_index=trajectory["sample_index"],
            rollout_n=trajectory["rollout_n"],
            validate=trajectory["validate"],
            name="agent_loop",
        ):
            assert agent_name in _agent_loop_registry, (
                f"Agent loop {agent_name} not registered, registered agent loops: {_agent_loop_registry.keys()}"
            )

            agent_loop_config = _agent_loop_registry[agent_name]
            agent_loop = hydra.utils.instantiate(
                config=agent_loop_config,
                trainer_config=_DummyConfig(config=self.config),
                server_manager=self.server_manager,
                tokenizer=self.tokenizer,
                processor=self.processor,
            )
            output: AgentLoopOutput = await agent_loop.run(sampling_params, **kwargs)
```

2. **Each trajectory gets traced** - The `rollout_trace_attr` context manager is called for **every single trajectory** with its metadata:
   - `step`: training step
   - `sample_index`: sample identifier
   - `rollout_n`: rollout number (for GROP algorithm where each sample can be rolled out n times)
   - `validate`: whether it's validation data

3. **All function calls within the trajectory are traced** - Functions decorated with `@rollout_trace_op` (like tool calls, LLM generation, etc.) will have their inputs and outputs recorded for each trajectory.

### Storage Considerations

Since ALL rollouts are traced:
- For 512 requests per batch, you'll get 512 separate traces
- Each trace includes all function calls, inputs, outputs, and timestamps for that trajectory
- This can generate substantial data volume (the documentation mentions "dozens of gigabytes per day")
- The Weave Free Plan has a 1GB monthly traffic limit, which can be quickly exceeded

### Trace Metadata

Each trace can be filtered and searched by:
- `step` - training step number
- `sample_index` - sample identifier
- `rollout_n` - rollout iteration
- `experiment_name` - experiment identifier
- `validate` - training vs validation dataset

This comprehensive tracing helps you understand the flow of multi-turn conversations, tool invocations, and model behavior for debugging and optimization purposes.

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### 👤 You

Where did you see "dozens of gigabytes per day"

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### 🤖 Cursor Assistant

I saw it in the verl documentation. Here's the exact quote:

```67:67:projects/week7/verl/docs/advance/rollout_trace.rst
The Weave Free Plan comes with a default monthly network traffic allowance of 1GB. During the training process, the amount of trace data generated is substantial, reaching dozens of gigabytes per day, so it is necessary to select an appropriate wandb plan.
```

This is from the rollout trace documentation file at `/Users/boj/ai-agent-book/projects/week7/verl/docs/advance/rollout_trace.rst`, specifically in the section about using wandb weave (line 67).

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