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Add comprehensive skill for building, testing, and debugging Hermes Agent RL environments for Atropos training. Includes: - SKILL.md: Full guide covering HermesAgentBaseEnv interface, required methods, config class, CLI modes (serve/process/evaluate), reward function patterns, common pitfalls, and minimum implementation checklist - New 'Inference Setup' section: instructs the agent to always ask the user for their inference provider (OpenRouter + model choice, self-hosted VLLM endpoint, or other OpenAI-compatible API) before running tests - references/agentresult-fields.md: AgentResult dataclass field reference - references/atropos-base-env.md: Atropos BaseEnv API reference - references/usage-patterns.md: Step-by-step patterns for process, evaluate, serve, and smoke test modes Will be auto-synced to ~/.hermes/skills/ via skills_sync.
65 lines
3 KiB
Markdown
65 lines
3 KiB
Markdown
# Atropos BaseEnv Reference
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Source: `atroposlib/envs/base.py` (~2124 lines)
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## Abstract Methods (MUST implement)
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| Method | Signature | Description |
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|--------|-----------|-------------|
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| `get_next_item()` | `async def get_next_item(self) -> Item` | Return next item for trajectory. Return None to pause. |
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| `evaluate()` | `async def evaluate(self, *args, **kwargs)` | Called every steps_per_eval steps. |
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| `setup()` | `async def setup(self)` | Called once at start. Load datasets, init models. |
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| `collect_trajectory()` | `async def collect_trajectory(self, item) -> Tuple[Optional[ScoredDataItem], List[Item]]` | Single rollout. Or override collect_trajectories instead. |
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## Overridable Methods
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| Method | Default Behavior | Override When |
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|--------|-----------------|---------------|
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| `collect_trajectories()` | Runs collect_trajectory group_size times in parallel | Batch generation, MCTS, coupled rollouts |
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| `wandb_log()` | Logs completion lengths, rollout table, perf stats | Add custom metrics (always call super) |
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| `config_init()` | Returns (env_config_cls(), ServerBaseline()) | Custom defaults + server configs |
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| `postprocess_histories()` | Passthrough | Final processing before sending to trainer |
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| `save_checkpoint()` | Saves JSON to checkpoint_dir | Custom serialization |
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| `cleanup()` | No-op | Release resources after each rollout |
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## ScoredDataGroup Structure
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```python
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ScoredDataGroup = TypedDict with:
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tokens: List[List[int]] # Token IDs per rollout
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masks: List[List[int]] # -100=prompt, token_id=completion
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scores: List[float] # Score per rollout
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advantages: Optional[...] # Per-token advantages
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ref_logprobs: Optional[...] # Reference model logprobs
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messages: Optional[...] # OpenAI-format messages
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inference_logprobs: Optional[...] # Inference logprobs
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```
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## BaseEnvConfig Key Fields
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| Field | Default | Description |
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|-------|---------|-------------|
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| `group_size` | 4 | Responses grouped for scoring |
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| `steps_per_eval` | 100 | Steps between evaluations |
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| `max_token_length` | 2048 | Max token length for generations |
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| `total_steps` | 1000 | Total training steps |
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| `use_wandb` | True | Enable wandb logging |
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| `tokenizer_name` | DeepHermes-3 | Tokenizer for token encoding |
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| `ensure_scores_are_not_same` | True | Skip groups with identical scores |
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| `worker_timeout` | 600 | Task timeout seconds |
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## Data Flow
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```
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env_manager() → add_train_workers() → handle_env()
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→ collect_trajectories() → postprocess_histories()
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→ handle_send_to_api() → training server
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```
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## Atropos Environment Statistics (82 environments analyzed)
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- 95% implement setup, collect_trajectories, evaluate, get_next_item
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- 76% override wandb_log
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- 54% have custom config class
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- Most use collect_trajectories (plural), not collect_trajectory (singular)
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- Common reward patterns: LLM-judge (~40), regex-extract (~35), code-exec (~12)
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