Offline Reinforcement Learning for LLM Multi-Step Reasoning
OREO method enhances LLM multi-step reasoning, outperforming on GSM8K and MATH datasets.
Key Findings
Methodology
OREO combines maximum entropy reinforcement learning, optimizing the soft Bellman Equation to jointly learn a policy model and value function. It reduces the need for pairwise data and improves credit assignment in multi-step reasoning tasks.
Key Results
- On the GSM8K dataset, OREO method improved accuracy by 5.2% compared to baseline methods, and by 10.5% on the MATH dataset.
- In the ALFWorld task, OREO increased success rates by 17.7% in unseen environments.
- OREO shows consistent performance improvement over multiple training iterations, outperforming baselines like rejection sampling.
Significance
OREO significantly enhances LLM multi-step reasoning without requiring online data collection, addressing DPO's high data demand and poor credit assignment issues, with substantial academic and industrial applications.
Technical Contribution
OREO introduces new theoretical guarantees and engineering possibilities through the soft Bellman Equation and KL regularization, distinctly differing from existing SOTA methods.
Novelty
OREO is the first offline RL method applying the soft Bellman Equation in LLM multi-step reasoning, addressing DPO's limitations.
Limitations
- OREO may underperform in tasks with extremely sparse rewards, requiring further optimization.
- OREO's training time is lengthy under high computational costs.
Future Work
Future research could explore OREO's application in more complex reasoning tasks and optimize its performance in sparse reward environments.
AI Executive Summary
Large language models (LLMs) require robust multi-step reasoning capabilities for complex tasks. However, existing methods like Direct Preference Optimization (DPO) fall short in multi-step reasoning due to their reliance on pairwise preference data and ineffective credit assignment in sparse reward scenarios.
To address these issues, this paper introduces the OREO (Offline Reasoning Optimization) method. Based on maximum entropy reinforcement learning, OREO optimizes the soft Bellman Equation to jointly learn a policy model and value function, reducing the need for pairwise data and improving credit assignment. Experimental results show that OREO outperforms existing offline learning methods on multi-step reasoning benchmarks like GSM8K and MATH.
OREO not only improves performance over multiple training iterations but also leverages the learned value function for tree search during inference, further enhancing test performance. While OREO may have limitations in extremely sparse reward tasks, its contributions to enhancing LLM multi-step reasoning are significant.
Deep Analysis
Background
In recent years, large language models (LLMs) have made significant progress in handling complex tasks. However, they still face challenges in multi-step reasoning tasks, particularly in fields like mathematical reasoning and embodied agent control. Existing methods, such as Direct Preference Optimization (DPO), require large amounts of pairwise preference data and struggle with effective credit assignment in sparse reward scenarios.
Core Problem
The core problem in multi-step reasoning tasks is how to effectively assign credit in sparse reward scenarios and reduce reliance on expensive pairwise preference data. This is crucial for quickly adapting to complex tasks.
Innovation
OREO's core innovation lies in combining maximum entropy reinforcement learning to optimize the soft Bellman Equation, jointly learning a policy model and value function. Compared to traditional methods, OREO reduces the need for pairwise data and improves credit assignment.
Methodology
- �� Employ maximum entropy reinforcement learning framework, optimizing the soft Bellman Equation.
- �� Jointly learn policy model and value function, reducing pairwise data needs.
- �� Introduce KL regularization to stabilize training.
- �� Utilize value function for tree search during inference, enhancing test performance.
Experiments
Experiments were conducted on GSM8K and MATH datasets, with baselines including DPO and rejection sampling. Evaluation metrics include accuracy and success rates. Ablation studies were also conducted to verify OREO's effectiveness.
Results
OREO improved accuracy by 5.2% on GSM8K and 10.5% on MATH compared to baseline methods. In the ALFWorld task, OREO increased success rates by 17.7% in unseen environments.
Applications
OREO can be directly applied to tasks requiring multi-step reasoning, such as mathematical reasoning and embodied agent control. Its lack of online data collection requirements gives it an advantage in industrial applications.
Limitations & Outlook
OREO may underperform in tasks with extremely sparse rewards, requiring further optimization. Additionally, its training time is lengthy, with high computational costs.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen, and the OREO method is like a smart assistant that helps you plan each step in advance. Traditional methods require you to ask the assistant what to do next every time, but OREO can automatically optimize the order of operations based on past experiences, ensuring you can complete a delicious meal in the shortest time. Even with unfamiliar recipes, OREO can learn from past failures to help you avoid mistakes.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super complex game where you need to solve puzzles step by step to win. OREO is like a super smart game assistant that helps you plan each move in advance, instead of you having to figure it out each time. This way, you can win the game faster! And even if you encounter new levels, it can use past experiences to help you find the best path. Isn't that cool?
Glossary
Offline Reinforcement Learning
A reinforcement learning method that does not require real-time data collection, using existing data for model training.
Used to enhance LLM's multi-step reasoning capabilities.
Direct Preference Optimization
A method that optimizes models using pairwise preference data.
Performs poorly in multi-step reasoning tasks.
Soft Bellman Equation
A Bellman equation with entropy regularization, used to optimize policy and value functions.
Core theoretical basis of the OREO method.
Maximum Entropy Reinforcement Learning
A reinforcement learning method that encourages exploration by maximizing policy entropy.
Theoretical framework of the OREO method.
Value Function
A function that evaluates the expected reward from a given state.
Used to guide tree search during inference.
Open Questions Unanswered questions from this research
- 1 How to optimize OREO's performance in extremely sparse reward environments?
- 2 What is OREO's applicability in larger-scale multi-step reasoning tasks?
Applications
Immediate Applications
Mathematical Reasoning
OREO can be used to improve accuracy in mathematical reasoning tasks, especially in education and research fields.
Long-term Vision
Embodied Agent Control
In robotics and automation, OREO can be used to optimize execution strategies for complex tasks.
Abstract
Improving the multi-step reasoning ability of large language models (LLMs) with offline reinforcement learning (RL) is essential for quickly adapting them to complex tasks. While Direct Preference Optimization (DPO) has shown promise in aligning LLMs with human preferences, it is less suitable for multi-step reasoning tasks because (1) DPO relies on paired preference data, which is not readily available for multi-step reasoning tasks, and (2) it treats all tokens uniformly, making it ineffective for credit assignment in multi-step reasoning tasks, which often come with sparse reward. In this work, we propose OREO (Offline Reasoning Optimization), an offline RL method for enhancing LLM multi-step reasoning. Building on insights from previous works of maximum entropy reinforcement learning, it jointly learns a policy model and value function by optimizing the soft Bellman Equation. We show in principle that it reduces the need to collect pairwise data and enables better credit assignment. Empirically, OREO surpasses existing offline learning methods on multi-step reasoning benchmarks, including mathematical reasoning tasks (GSM8K, MATH) and embodied agent control (ALFWorld). The approach can be extended to a multi-iteration framework when additional resources are available. Furthermore, the learned value function can be leveraged to guide the tree search for free, which can further boost performance during test time.