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Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization (2410.09302v2)

Published 11 Oct 2024 in cs.LG, cs.AI, and cs.CL

Abstract: Reinforcement Learning (RL) plays a crucial role in aligning LLMs with human preferences and improving their ability to perform complex tasks. However, current approaches either require significant computational resources due to the use of multiple models and extensive online sampling for training (e.g., PPO) or are framed as bandit problems (e.g., DPO, DRO), which often struggle with multi-step reasoning tasks, such as math problem solving and complex reasoning that involve long chains of thought. To overcome these limitations, we introduce Direct Q-function Optimization (DQO), which formulates the response generation process as a Markov Decision Process (MDP) and utilizes the soft actor-critic (SAC) framework to optimize a Q-function directly parameterized by the LLM. The MDP formulation of DQO offers structural advantages over bandit-based methods, enabling more effective process supervision. Experimental results on two math problem-solving datasets, GSM8K and MATH, demonstrate that DQO outperforms previous methods, establishing it as a promising offline reinforcement learning approach for aligning LLMs.

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Citations (1)
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Summary

  • The paper introduces a novel Direct Q-function Optimization (DQO) framework that reframes language generation as a multi-step MDP to boost reasoning.
  • It leverages the soft actor-critic method and process reward models to provide detailed supervision during complex task execution.
  • Experiments on GSM8K and MATH datasets demonstrate that DQO outperforms traditional RL methods in multi-step reasoning tasks.

Overview of "Enhancing Multi-Step Reasoning Abilities of LLMs through Direct Q-Function Optimization"

The paper "Enhancing Multi-Step Reasoning Abilities of LLMs through Direct Q-Function Optimization" presents an innovative approach to improving the reasoning capabilities of LLMs by introducing a novel framework called Direct Q-function Optimization (DQO). This work aims to address the limitations of existing reinforcement learning (RL) methods in handling tasks that require multi-step reasoning, such as mathematical problem-solving and tasks requiring complex, sequential thought processes.

Motivation

The authors identify several challenges with current RL paradigms used for aligning LLMs with human preferences. Traditional methods like Proximal Policy Optimization (PPO) demand extensive computational resources due to their reliance on multiple models and extensive online data sampling. On the other hand, bandit-based approaches often fall short in managing intricate multi-step reasoning tasks due to their simplistic formulation as single-step decision processes. The inherent complexity of tasks requiring a sequence of logical steps cannot be fully encapsulated within these frameworks.

Methodology

Direct Q-function Optimization (DQO) is proposed to overcome these challenges. DQO reframes the response generation process as a Markov Decision Process (MDP) and employs the soft actor-critic (SAC) framework to optimize a Q-function directly parameterized by the LLM itself. This novel formulation allows DQO to exploit the structural advantages of MDPs over bandit models, enabling more effective supervision through the reasoning process. A key aspect of DQO is its ability to incorporate process reward models (PRMs), which provide intermediate rewards that elucidate the critical stages in reasoning where mistakes may occur, thus offering stronger supervision signals.

Experimental Results

The efficacy of DQO is demonstrated through experiments conducted on math problem-solving datasets, specifically GSM8K and MATH. The results indicate that DQO significantly outperforms previous methods in aligning LLMs for complex reasoning tasks, highlighting its potential as a superior offline reinforcement learning approach. The empirical evaluation showcases that DQO's ability to exploit process rewards contributes to its enhanced performance, reaffirming the advantages of formulating language generation as a multi-step MDP.

Implications and Future Directions

The implications of this research are twofold. Practically, DQO provides a more efficient and effective method for aligning LLMs with intricate reasoning tasks, circumventing the pitfalls of traditional RL techniques. Theoretically, it pushes the boundaries in understanding how complex reasoning tasks can be modeled within the RL framework, especially in the context of large-scale LLMs.

Looking forward, the proposed DQO framework opens avenues for further exploration in enhancing reasoning capabilities across various domains where LLMs are applied. It also sets a precedent for integrating process-level rewards into the training of LLMs, which could revolutionize the alignment and training strategies for future AI systems. Further research might explore the application of DQO in different reasoning-intensive tasks beyond mathematics, potentially leading to advancements in general AI reasoning and problem-solving efficiency. Additionally, the findings could be instrumental in refining RL-based alignment with human intents, particularly in the burgeoning field of AI safety and ethical AI development.

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