Awesome Credit Assignment in LLM RL
What it is
A comprehensive research repository and survey map for credit assignment in Reinforcement Learning for Large Language Models (LLM RL). It provides a taxonomy of 47+ methods categorized by granularity (token, step, turn) and methodology (Monte Carlo, Information-Theoretic, etc.) to address how sparse rewards are distributed across reasoning chains and agentic actions.
The problem it solves
The difficulty of determining which specific token, step, or action in a long sequence (e.g., a 100k token agentic task) is responsible for a final success or failure.
What you could use it for
- Identifying specific credit assignment methods for long-horizon reasoning tasks.
- Navigating a taxonomy of techniques for agentic RL (e.g., turn-level process reward models).
- Finding benchmarks and datasets for evaluating credit assignment.
- Mapping out the state-of-the-art in reward shaping and information-theoretic approaches for LLMs.
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