Offline Reinforcement Learning for LLM Multi-Step Reasoning (2412.16145v2)

Abstract: Improving the multi-step reasoning ability of 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.

• The paper introduces OREO, an offline RL technique that improves multi-step reasoning by jointly optimizing policy and value functions using a soft Bellman equation.
• It demonstrates significant performance gains, achieving 52.5% accuracy on MATH and outperforming traditional offline learning methods.
• OREO minimizes reliance on costly pairwise data, enabling iterative self-improvement and efficient handling of complex, multi-step tasks.

Insights into Offline Reinforcement Learning for LLM Multi-Step Reasoning

This paper introduces a novel approach called Offline Reasoning Optimization (OREO), targeting the enhancement of multi-step reasoning capabilities in LLMs using offline reinforcement learning (RL). Multi-step reasoning is crucial for LLMs as it facilitates their performance on complex tasks such as mathematical problem solving and embodied agent controlling, which often require intricate logical reasoning processes.

Challenges in Existing Methods

The paper critiques existing methods like Direct Preference Optimization (DPO) that, while promising, face practical challenges. Specifically, DPO's reliance on pairwise preference data is costly and inefficient for multi-step reasoning tasks that inherently benefit from more granular credit assignments. Moreover, conventional RL techniques often necessitate costly online data collection, further hindering rapid adaptability to new tasks.

Methodology: OREO

OREO leverages principles from maximum entropy reinforcement learning to overcome these challenges. It jointly optimizes a policy model alongside a value function by addressing the soft BeLLMan equation. This methodology reduces the dependency on pairwise data and facilitates refined credit assignment across different steps of reasoning. Consequently, OREO promotes self-improvement without repetitively relying on human-labeled data, making it a practical alternative to online RL algorithms.

The OREO algorithm is built upon two key components:

1. Policy Model: It determines actions that maximize cumulative reward.
2. Value Function: It offers a detailed assessment of states based on the expected future rewards, enabling precise credit assignments.

The soft BeLLMan equation serves as a foundational element, ensuring that deviations in value between consecutive actions are aligned with the reward signals, adjusted for the regularization encouraging closeness between the learned policy and a reference policy.

Empirical Evaluation

OREO's efficacy is demonstrated by surpassing traditional offline learning methods in standard benchmarks. In mathematical reasoning tasks such as GSM8K and MATH, and embodied agent control tasks like ALFWorld, OREO consistently outperforms baselines, displaying a higher accuracy in problem-solving. For instance, using a 1.5 billion parameter model, OREO achieved an impressive 52.5% accuracy on the MATH dataset, marking significant improvements over baseline figures.

Strategic Implications

One of the notable contributions of OREO is its ability to extend into iterative frameworks, allowing for enhanced performance with supplementary resources. Moreover, by using the learned value function to guide tree searches, OREO can further boost decision-making efficiency during inference. This offers a practical edge in real-time applications where decision speed is crucial.

Theoretical and Future Directions

From a theoretical standpoint, OREO successfully bridges gaps between policy-based and value-based learning, presenting a cohesive offline RL approach. Its adaptability implies potential applicability beyond LLM reasoning tasks, possibly extending to areas such as coding and web-navigation tasks which similarly require complex multi-step logic.

One future direction is the exploration of integrating OREO with more diverse multi-step reasoning tasks, which could uncover additional strengths and potential areas of improvement. Additionally, further research could investigate the scalability of OREO to larger models and its impact on broader AI tasks.

In conclusion, OREO presents a significant advancement in the offline RL domain, pushing the boundaries of how LLMs can be efficiently and effectively trained for complex reasoning tasks. This paper sets the stage for future exploration into leveraging offline methodologies to enhance LLM capabilities systematically.