rePIRL: Learn PRM with Inverse RL for LLM Reasoning
rePIRL learns process reward models via inverse RL to enhance LLM reasoning.
Key Findings
Methodology
rePIRL employs an inverse RL framework with a dual learning process that alternately updates the policy and the process reward model. It minimizes assumptions about expert policies and uses customized techniques to address scaling challenges in traditional inverse RL for LLMs.
Key Results
- On standard math and coding reasoning datasets, rePIRL outperforms existing methods, enhancing model generalizability and reasoning accuracy.
- In test-time training and scaling, rePIRL provides early signals effectively addressing hard problem training.
- A detailed ablation study validates the training recipe and key design choices.
Significance
By minimizing assumptions about expert policies, rePIRL offers a unified method for both online and offline PRM learning, addressing limitations of existing methods. Its superior performance on standard datasets demonstrates its potential in complex reasoning tasks.
Technical Contribution
rePIRL extends traditional inverse RL to LLMs, providing new theoretical guarantees and engineering possibilities, especially in learning process reward models.
Novelty
rePIRL is the first to generalize inverse RL for LLM reasoning, unifying existing PRM learning methods while reducing reliance on expert policies.
Limitations
- In some cases, rePIRL may face high computational costs, especially on large datasets.
- Sensitive to model parameter choices, which may affect result stability.
Future Work
Future work could explore rePIRL's application in more domains and further optimize the algorithm to reduce computational costs.
AI Executive Summary
In complex LLM reasoning tasks, existing methods often rely on strong assumptions about expert policies, limiting model generalizability. rePIRL introduces a novel process reward model learning method via an inverse reinforcement learning framework, enhancing reasoning capabilities without relying on expert policies.
rePIRL employs a dual learning process, alternately updating the policy and process reward model, addressing challenges in scaling traditional inverse RL to LLMs. Experiments on standard math and coding reasoning datasets demonstrate rePIRL's superiority in improving model reasoning accuracy and generalizability.
Despite rePIRL's strong performance, computational costs on large datasets remain a challenge. Future research could explore its application in more domains and further optimize the algorithm to reduce computational costs.
Deep Analysis
Background
Recent advances in deep reinforcement learning have shown significant progress in simulated games, Go, and poker, with process rewards playing a crucial role. However, in LLM reasoning, existing methods often rely on strong assumptions about expert policies, limiting model generalizability.
Core Problem
Existing process reward model learning methods either rely on strong assumptions about expert policies or have intrinsic limitations in scalability, making it challenging to perform well in complex LLM reasoning tasks.
Innovation
rePIRL introduces a novel process reward model learning method via an inverse reinforcement learning framework, enhancing reasoning capabilities without relying on expert policies, addressing limitations of existing methods.
Methodology
- �� Design a dual learning process that alternately updates the policy and process reward model.
- �� Apply customized techniques to address scaling challenges in traditional inverse RL for LLMs.
- �� Theoretically prove the framework can unify both online and offline PRM learning methods.
Experiments
Experiments conducted on standard math and coding reasoning datasets, using Qwen2.5-3B and Qwen3-4B as base models, compare rePIRL with existing baseline methods.
Results
rePIRL demonstrates superior performance on standard datasets, enhancing model generalizability and reasoning accuracy, particularly in providing effective early signals in test-time training and scaling.
Applications
rePIRL can be applied to complex reasoning tasks requiring process reward models, particularly without relying on expert policies.
Limitations & Outlook
Despite rePIRL's strong performance, computational costs on large datasets remain a challenge. Future work could explore further optimizing the algorithm to reduce computational costs.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. Traditional methods are like following a recipe step by step, but if the recipe is incomplete or doesn't suit your taste, you run into trouble. rePIRL is like a smart assistant that observes your cooking process, learns your preferences, and then offers suggestions to make dishes more to your liking. It doesn't rely on a fixed recipe but optimizes the process through observation and learning.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super complex game, and everyone tells you what to do, but their advice isn't always right. rePIRL is like your game assistant, watching how you play and giving better suggestions to help you win. It doesn't rely on others' advice but learns and helps you get stronger through observation!
Glossary
Inverse Reinforcement Learning
A method for learning a reward function by observing expert behavior, typically used in environments without explicit reward signals.
Used for learning process reward models.
Process Reward Model
A model that provides intermediate rewards during task execution to help optimize the learning process.
Provides intermediate rewards in LLM reasoning.
Maximum Entropy Reinforcement Learning
A reinforcement learning method that optimizes policies by maximizing the combination of reward and policy entropy.
Used for updating the policy model.
Monte Carlo Tree Search
A search algorithm used for decision processes, finding optimal solutions by simulating and evaluating different strategies.
Used for generating expert policy process reward labels.
Policy Optimization
A method for improving model performance by optimizing policies, commonly used in reinforcement learning.
Used for updating the policy model.
Open Questions Unanswered questions from this research
- 1 How to improve rePIRL's scalability without increasing computational costs?
- 2 How does rePIRL perform in more complex reasoning tasks?
- 3 How to further reduce reliance on expert policies?
Applications
Immediate Applications
Complex Reasoning Tasks
rePIRL can be applied to complex reasoning tasks requiring process rewards, helping improve model reasoning capabilities and accuracy.
Long-term Vision
General AI
By optimizing process reward models, rePIRL has the potential to contribute to general AI, driving the development of more intelligent AI systems.
Abstract
Process rewards have been widely used in deep reinforcement learning to improve training efficiency, reduce variance, and prevent reward hacking. In LLM reasoning, existing works also explore various solutions for learning effective process reward models (PRM) with or without the help of an expert policy. However, existing methods either rely on strong assumptions about the expert policies (e.g., requiring their reward functions) or suffer intrinsic limitations (e.g., entropy collapse), resulting in weak PRMs or limited generalizability. In this paper, we introduce rePIRL, an inverse RL-inspired framework that learns effective PRMs with minimal assumptions about expert policies. Specifically, we design a dual learning process that updates the policy and the PRM interchangeably. Our learning algorithm has customized techniques to address the challenges of scaling traditional inverse RL to LLMs. We theoretically show that our proposed learning framework can unify both online and offline PRM learning methods, justifying that rePIRL can learn PRMs with minimal assumptions. Empirical evaluations on standardized math and coding reasoning datasets demonstrate the effectiveness of rePIRL over existing methods. We further show the application of our trained PRM in test-time training, test-time scaling, and providing an early signal for training hard problems. Finally, we validate our training recipe and key design choices via a detailed ablation study.