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Reward Functions Overview

This guide provides an overview of reward functions in RewardKit and how to use them effectively.

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What are Reward Functions?

Reward functions are the core building blocks of RewardKit. They evaluate model responses and provide numerical feedback that can be used for training, evaluation, and analysis.

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Basic Structure

Every reward function follows this pattern: 
from reward_kit import reward_function

@reward_function
def my_reward(response: str, expected_response: str, **kwargs) -> float:
    # Your evaluation logic here
    return score

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Built-in Reward Functions

RewardKit provides several built-in reward functions:

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Accuracy-based

  • accuracy: Exact string matching
  • fuzzy_accuracy: Fuzzy string matching with configurable threshold

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Length-based

  • length: Evaluates response length
  • length_penalty: Penalizes responses that are too long or short

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Format-based

  • json_schema: Validates JSON responses against a schema
  • format_compliance: Checks if responses follow expected formats

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Code-specific

  • code_execution: Evaluates code by executing it
  • syntax_validation: Checks code syntax

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Custom Reward Functions

You can create custom reward functions for domain-specific evaluation: 
@reward_function
def domain_specific_reward(response: str, expected_response: str) -> float:
    # Custom logic for your domain
    score = 0.0

    # Example: Check for specific keywords
    if "important_keyword" in response.lower():
        score += 0.5

    # Example: Length consideration
    if 50 <= len(response) <= 200:
        score += 0.3

    # Example: Accuracy component
    if response.strip() == expected_response.strip():
        score += 0.2

    return min(score, 1.0)  # Cap at 1.0

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Best Practices

  1. Clear Naming: Use descriptive names for your reward functions
  2. Type Hints: Include type hints for better IDE support
  3. Documentation: Add docstrings explaining what your function evaluates
  4. Normalization: Normalize scores to a consistent range (typically 0.0 to 1.0)
  5. Testing: Test your reward functions with edge cases

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Combining Rewards

You can combine multiple reward functions: 
@reward_function
def combined_reward(response: str, expected_response: str) -> float:
    acc_score = accuracy(response, expected_response)
    len_score = length_penalty(response, min_length=10, max_length=100)

    return 0.8 * acc_score + 0.2 * len_score

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Next Steps