DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs (2505.07049v1)

Abstract: We propose DialogueReason, a reasoning paradigm that uncovers the lost roles in monologue-style reasoning models, aiming to boost diversity and coherency of the reasoning process. Recent advances in RL-based large reasoning models have led to impressive long CoT capabilities and high performance on math and science benchmarks. However, these reasoning models rely mainly on monologue-style reasoning, which often limits reasoning diversity and coherency, frequently recycling fixed strategies or exhibiting unnecessary shifts in attention. Our work consists of an analysis of monologue reasoning patterns and the development of a dialogue-based reasoning approach. We first introduce the Compound-QA task, which concatenates multiple problems into a single prompt to assess both diversity and coherency of reasoning. Our analysis shows that Compound-QA exposes weaknesses in monologue reasoning, evidenced by both quantitative metrics and qualitative reasoning traces. Building on the analysis, we propose a dialogue-based reasoning, named DialogueReason, structured around agents, environment, and interactions. Using PPO with rule-based rewards, we train open-source LLMs (Qwen-QWQ and Qwen-Base) to adopt dialogue reasoning. We evaluate trained models on MATH, AIME, and GPQA datasets, showing that the dialogue reasoning model outperforms monologue models under more complex compound questions. Additionally, we discuss how dialogue-based reasoning helps enhance interpretability, facilitate more intuitive human interaction, and inspire advances in multi-agent system design.

An Examination of DialogueReason: Enhancing Reasoning Diversity and Coherency in LLMs

The research paper "DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs" introduces DialogueReason as an innovative reasoning framework designed to address the deficiencies associated with the prevalent monologue-style reasoning models in LLMs. While monologue reasoning has demonstrated impressive capabilities in CoT reasoning, particularly in domains like mathematics and science, it suffers from limitations in reasoning diversity and coherency. These constraints manifest as fixed strategy recycling and frequent shifts in attention, thereby reducing the model's effectiveness in handling complex tasks that require diverse approaches and stable reasoning paths.

Key Contributions

1. Compound-QA Task: The authors introduce Compound-QA as a novel task aimed at evaluating the diversity and coherency of reasoning models. By merging multiple sub-questions into a single prompt, Compound-QA challenges models to demonstrate flexible strategy application and sustained focus across diverse reasoning paths. This task has revealed the performance degradation in monologue-based models, particularly as the complexity of compound questions increases.
2. Dialogue-Based Reasoning Framework: DialogueReason is framed as an interactive reasoning process consisting of distinct agents, environment, and interaction settings. This framework promotes the adoption of diverse reasoning strategies and enhances coherency through structured dialogues among reasoning agents, improving interpretability and user interaction potential.
3. Rule-Based Reinforcement Learning (RL) Training: Using PPO combined with rule-based reward functions, the researchers train their models to transition from monologue-style reasoning to the dialogue-based approach. The training involved Qwen-series LLMs, significantly enhancing their capacity to handle compound reasoning tasks with improved accuracy and coherency.

Empirical Findings

The authors conducted comparative evaluations across several datasets, including MATH, AIME, and GPQA, demonstrating that DialogueReason outperforms traditional monologue models in complex compound questions. Notably, as the number of questions in a compound task increases, DialogueReason maintains higher accuracy and exhibits stronger robustness, underscoring its enhanced flexibility and cohesiveness.

Theoretical and Practical Implications

The introduction of DialogueReason holds substantial theoretical implications, particularly in reinforcing the role of multi-agent systems in reasoning and reducing the inherent rigidity in monologue models. Practically, this framework promises improved reasoning interpretability and adaptability, potentially inspiring developments in multi-agent system design. Additionally, by integrating dialogue-style reasoning, LLMs become better suited for tasks requiring broader and more cohesive cognitive representations, such as interactive applications and user-guided explorations.

Future Prospects

The research identifies areas for future exploration, such as expanding the dialogue framework to include diverse reasoning tasks across various domains and disentangling the evaluation metrics further to ascertain diversity and coherency impacts independently. Moreover, the potential of leveraging Compound-QA not just as an evaluation tool but also as a training objective remains an intriguing prospect, potentially facilitating more robust and adaptable reasoning models.

In conclusion, DialogueReason exemplifies a progressive step toward more versatile and coherent reasoning methodologies, addressing critical gaps in existing LLM frameworks. By promoting structured dialogues and leveraging RL techniques, this research provides a compelling case for adopting multi-agent interactions in advanced reasoning models.