Single-turn environments

A single-turn environment resolves each example in one model response - prompt in, completion out, reward computed. You implement a prompt builder and a reward on an EnvironmentSingleTurn subclass. Import these public names in environment code:

from freesolo.datasets import TaskExample
from freesolo.datasets.records import load_task_examples
from freesolo.environments import (
    EnvironmentEpisode,
    EnvironmentMultiTurn,
    EnvironmentStepResult,
    EnvironmentSingleTurn,
    RewardResult,
)

For a single-turn task, subclass EnvironmentSingleTurn:

environment.py

from pathlib import Path

from freesolo.datasets import TaskExample
from freesolo.datasets.records import load_task_examples
from freesolo.environments import EnvironmentSingleTurn, RewardResult

ROOT = Path(__file__).parent

class MathEnv(EnvironmentSingleTurn):
    def __init__(self, *, split: str = "train") -> None:
        self.dataset = load_task_examples(ROOT / "dataset" / f"{split}.jsonl")

    def build_prompt_messages(self, example: TaskExample, prompt_text: str):
        return [{"role": "user", "content": example.input}]

    def score_response(self, example: TaskExample, response_text: str) -> RewardResult:
        expected = str(example.output or "").strip()
        score = 1.0 if expected and expected in response_text else 0.0
        return RewardResult(score=score, threshold=1.0)

def load_environment(split: str = "train", **kwargs) -> MathEnv:
    return MathEnv(split=split)

The dataset file uses the input/output row shape (Task records). load_task_examples(...) exposes each record’s input/output as example.input/example.output, with the raw row available as example.record. For SFT, the SDK builds the training conversation from the environment’s start_episode(example, prompt_text) plus sft_completion(example). The default sft_completion converts example.output into completion messages. See Datasets for the scalar and message-shaped output forms. Override sft_completion only when the gold completion must be synthesized from other fields. Single-turn environments usually implement build_prompt_messages. The SDK’s default start_episode calls it and prepends the run’s prompt text as a system message when your messages lack one, so local eval, SFT, and GRPO see the same policy prompt. Multi-turn environments own start_episode; put any task-specific initial system message there. On managed runs, Flash applies the same guarantee to every episode’s opening messages, single- and multi-turn: if start_episode returns no system message (or an empty one), the run’s prompt text becomes the system message. When a run uses thinking = true, score_response receives answer text by default. response_text stays string-compatible and also exposes response_text.completion, response_text.thinking, and response_text.raw for rewards that inspect the reasoning trace.

Pass parameters

If your load_environment(**kwargs) accepts arguments, set them under [environment.params]:

[environment]
id = "your-org/your-env"

[environment.params]
difficulty = "hard"
num_examples = 500

Get started

Flash

Reference

Single-turn environments

Pass parameters

Next steps

Multi-turn environments

Datasets

​Pass parameters

​Next steps

Multi-turn environments

Datasets

Pass parameters

Next steps