version: "1.0.3" name: design-environment description: Design a benchmax environment, its ordered dataset, tools, and explicit reward shape for a Castform project.

Design an environment

Before coding, inspect the maintained examples, choose the closest task shape, and follow its README.md and main.py.

Use BaseEnv for the standard OpenAI-compatible chat and tool loop. Use HarborEnv for a Harbor dataset/package or tasks that ship their own instruction, sandbox, and verifier. Start with aime for packaged tasks or harvey for a custom harness. Do not infer Harbor from a judge, tool, or Dockerfile alone. Extend Environment directly only for another genuinely different rollout loop.

Required BaseEnv shape

from pathlib import Path
from benchmax.envs import BaseEnv, BaseRollout, DatasetSplit, JsonlDataset
from benchmax.envs.base import resolve_dataset_path
from benchmax.rewards import extract_completion_text
class MyEnv(BaseEnv):
    max_turns = 1
    async def create_dataset(
        self, split: DatasetSplit, base_dir: Path
    ) -> JsonlDataset:
        path = resolve_dataset_path(base_dir, f"{split}.jsonl")
        return JsonlDataset(path, row_to_example=...)
    async def compute_reward(self, rollout: BaseRollout) -> dict[str, float]:
        answer = extract_completion_text(rollout.messages)
        return {"correct": float(answer == rollout.example_args["answer"])}

Build each Example with a stable ID, normally canonical_example_id(payload). prompt_messages is the reserved BaseEnv payload field; all other fields become rollout.example_args. Put system messages in prompt_messages rather than ambient module state.

Dataset is an ordered base class, not a cleaning pipeline. The environment owns the runtime representation and can store lightweight references in payloads. Preparation, cleaning and QA generation belong in the project data script.

Reward contract

• Return named reward components from successful individual and group reward hooks.
• Return finite numbers and make correctness the dominant signal.
• Let judge, model, tool and sandbox operational failures propagate through the

typed runtime path. benchmax logs them and returns no rewards with a non-finished termination reason.

• Do not catch a judge failure and report it as a legitimate score.
• Programming, malformed-result and configuration errors should remain loud.

Override compute_group_rewards only when scoring genuinely depends on successful siblings. Failed siblings are excluded from group-relative scoring, and a failed group judge zeroes otherwise-successful siblings without cancelling the group.

Optional tools

BaseEnv supplies no tools by default. A tool-using environment returns standard OpenAI tool schemas from list_tools and dispatches them in run_tool:

async def run_tool(self, rollout_id: str, tool_name: str, **tool_args):
    if tool_name != "lookup":
        raise ValueError(f"unknown tool: {tool_name}")
    return await self.lookup(tool_args["query"])

Keep clients pickle-safe. Use InjectedAuth for calls through the Castform LLM endpoint so Castform supplies the current session credential. Use explicit StaticBearerAuth for a user-managed external endpoint; never read Castform credentials from benchmax environment code.

Model-request ownership

Treat model sampling as trainer-owned. A harness may request an output ceiling with max_tokens or max_completion_tokens; static validation emits a warning because Castform may clamp that ceiling to the remaining context budget. Do not set temperature, top_p, top_k, penalties, seed, or stop in agent or nested model kwargs. Static validation rejects them instead of allowing a later training failure. It also rejects unsupported controls such as n > 1, forced tool_choice, logprobs, and non-text response formats.

Review before handoff

1. Test dataset identity and split ordering.
2. Unit-test empty, wrong, partial and correct completions against every reward key.
3. Exercise tool errors and judge errors and confirm zero rewards plus an explicit

termination reason and log.

1. Load verify-environment and run the real two-sibling validation.
2. Record every remote runtime import in RUNTIME_DEPENDENCIES for launch-run.

For Harbor, require sandbox credentials and the matching provider extra in the bundle dependencies, for example harbor[modal]>=0.18,<0.19.