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name: add-reward description: Guide for adding a new reward function to AReaL. Use when user wants to create a reward function.

Add Reward

Add a new reward function to AReaL.

When to Use

This skill is triggered when:

• User asks "how do I add a reward function?"
• User wants to implement custom rewards
• User mentions reward computation

Step-by-Step Guide

Step 1: Create Reward File

Create areal/reward/<name>.py:

from typing import Any

from areal.utils import logging

logger = logging.getLogger("MyReward")


def <name>_reward_fn(
    prompt: str,
    completions: str,
    prompt_ids,
    completion_ids,
    answer: str | None = None,
    **kwargs: Any,
) -> float:
    """Compute reward for a single completion.

    Args:
        prompt: Prompt string
        completions: Completion string (model output)
        prompt_ids: Tokenized prompt IDs
        completion_ids: Tokenized completion IDs
        answer: Ground truth answer from dataset (optional)
        **kwargs: Additional data from dataset

    Returns:
        Reward value (float), typically 0.0 or 1.0
    """
    try:
        # Extract answer from completion
        extracted = _extract_answer(completions)

        # Compare with ground truth
        if answer is not None and extracted == str(answer):
            return 1.0
        return 0.0
    except Exception:
        logger.warning("Exception in reward computation", exc_info=True)
        return 0.0


def _extract_answer(completion: str) -> str:
    """Extract the answer from a completion string.

    Implement your extraction logic here.
    """
    # Example: Extract content from \boxed{}
    import re

    match = re.search(r"\\boxed\{([^}]+)\}", completion)
    if match:
        return match.group(1).strip()
    return completion.strip()

Step 2: Register in init.py

Update areal/reward/__init__.py:

# Add to VALID_REWARD_FN
VALID_REWARD_FN = [
    # ... existing reward functions
    "<name>",
]

# Add to get_reward_fn function
def get_reward_fn(name: str, **kwargs):
    # ... existing code
    elif name == "<name>":
        from areal.reward.<name> import <name>_reward_fn
        return <name>_reward_fn

Step 3: Handle Blocking Operations

If your reward function uses blocking operations (e.g., API calls, model inference), the workflow will wrap it with AsyncRewardWrapper:

# In your workflow
from areal.reward import AsyncRewardWrapper

self.reward_fn = AsyncRewardWrapper(reward_fn)

# Then call it asynchronously
rewards = await self.reward_fn(prompt, completions, **data)

Step 4: Add Tests

Create tests/test_<name>_reward.py:

import pytest
from areal.reward.<name> import <name>_reward_fn

def test_reward_correct_answer():
    reward = <name>_reward_fn(
        prompt="What is 2+2?",
        completions="The answer is \\boxed{4}",
        prompt_ids=None,
        completion_ids=None,
        answer="4",
    )
    assert reward == 1.0

def test_reward_wrong_answer():
    reward = <name>_reward_fn(
        prompt="What is 2+2?",
        completions="The answer is \\boxed{5}",
        prompt_ids=None,
        completion_ids=None,
        answer="4",
    )
    assert reward == 0.0

Reference Implementations

Reward	File	Description
GSM8K	areal/reward/gsm8k.py	Math answer verification
Geometry3K	areal/reward/geometry3k.py	Geometry answer verification
CLEVR	areal/reward/clevr_count_70k.py	Counting verification
MathVerify	areal/reward/math_verify.py	General math verification

Function Signature

All reward functions must follow this signature:

def reward_fn(
    prompt: str,               # Input prompt string
    completions: str,          # Model completion string
    prompt_ids,                # Tokenized prompt
    completion_ids,            # Tokenized completion
    **kwargs: Any,             # Additional data from dataset (e.g., answer)
) -> float:                    # Reward value (typically 0.0 or 1.0)

Note: The reward function is called once per sample. Batching is handled by AsyncRewardWrapper in the workflow.

Key Requirements

1. Deterministic: Same inputs should produce same outputs
2. Return float: Output is a single float value per sample
3. No blocking in async context: Use AsyncRewardWrapper if needed
4. Logging: Use areal.utils.logging, not print
5. Handle exceptions: Return 0.0 on error, don't raise

Common Mistakes

• Returning a tensor instead of a float
• Expecting batched inputs (reward is called per sample)
• Non-deterministic behavior
• Blocking operations without AsyncRewardWrapper
• Raising exceptions instead of returning 0.0

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