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Use reward functions when you can score model outputs programmatically — for example, checking JSON validity, comparing against a ground truth, or running unit tests. Decorate your function with @osmosis_reward and return a float between 0 and 1.

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Basic Example

File: reward_fn/compute_reward.py 
import re
from osmosis_ai import osmosis_reward

def extract_solution(solution_str):
    solution = re.search(r'####\s*([-+]?\d*\.?\d+)', solution_str)
    if not solution:
        return None
    return solution.group(1)

@osmosis_reward
def numbers_match_reward(
    solution_str: str,
    ground_truth: str,
    extra_info: dict = None,
    **kwargs
) -> float:
    """
    Reward function that checks if extracted number matches ground truth.
    Returns 1.0 for match, 0.0 otherwise.
    """
    extracted = extract_solution(solution_str)
    try:
        sol_val = float(extracted)
        gt_val = float(ground_truth)
        return 1.0 if abs(gt_val - sol_val) < 1e-7 else 0.0
    except:
        return 0.0
See API Reference for the full function signature and parameter details.

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Common Patterns

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Exact Match

@osmosis_reward
def exact_match_reward(
    solution_str: str,
    ground_truth: str,
    extra_info: dict = None,
    **kwargs
) -> float:
    """Returns 1.0 for exact match, 0.0 otherwise"""
    return 1.0 if solution_str.strip() == ground_truth.strip() else 0.0

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Multi-Criteria

import json

@osmosis_reward
def multi_criteria_reward(
    solution_str: str,
    ground_truth: str,
    extra_info: dict = None,
    **kwargs
) -> float:
    """Evaluates multiple criteria and returns weighted average"""
    try:
        solution = json.loads(solution_str)
        expected = json.loads(ground_truth)

        scores = []

        # Criterion 1: Correctness (weight: 0.5)
        correctness = 1.0 if solution["answer"] == expected["answer"] else 0.0
        scores.append(correctness * 0.5)

        # Criterion 2: Explanation quality (weight: 0.3)
        explanation_length = len(solution.get("explanation", ""))
        explanation_score = min(explanation_length / 100, 1.0)
        scores.append(explanation_score * 0.3)

        # Criterion 3: Code validity (weight: 0.2)
        has_code = "code" in solution and len(solution["code"]) > 0
        scores.append(1.0 * 0.2 if has_code else 0.0)

        return sum(scores)
    except:
        return 0.0
Test your reward functions locally before pushing. See Best Practices for testing patterns.

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Next Steps

Reward Rubrics

Learn about LLM-based evaluation

GitHub Integration

Connect your repository to Osmosis