Deliberate Reasoning in Language Models as Structure-Aware Planning with an Accurate World Model (2410.03136v3)

Abstract: Enhancing the reasoning capabilities of LMs remains a key challenge, especially for tasks that require complex, multi-step decision-making where existing Chain-of-Thought (CoT) approaches struggle with consistency and verification. In this paper, we propose a novel reasoning framework, referred to as Structure-aware Planning with an Accurate World Model (SWAP), that integrates structured knowledge representation with learned planning. Unlike prior methods that rely purely on natural language reasoning, SWAP leverages entailment graphs to encode structured dependencies and enable symbolic verification of intermediate steps. To systematically construct and update the graph, SWAP employs a policy model to propose candidate expansions and a world model to predict structural updates. To improve accuracy, the world model generates multiple alternative updates, and a discriminator re-ranks them based on plausibility. To encourage diverse exploration, we introduce Diversity-based Modelling (DM), which samples candidates from the remaining probability mass after removing previously sampled candidates from the original policy distribution. Additionally, SWAP improves the discrimination accuracy through Contrastive Ranking (CR), which directly compares candidates within prompts and incorporates meta-knowledge to improve ranking quality. We evaluate SWAP across diverse reasoning-intensive benchmarks including math reasoning, logical reasoning, and coding tasks. Extensive experiments demonstrate that SWAP significantly improves upon the base models and consistently outperforms existing reasoning methods.

• The paper introduces SWAP, a framework that enhances LLM reasoning through structure-aware planning guided by an accurate world model, diverging from traditional token prediction.
• SWAP utilizes technical components like entailment graphs for structural reasoning, a Generator-Discriminator architecture for state prediction and consistency, diversity-based modeling, and contrastive ranking.
• Experimental results across diverse reasoning benchmarks show significant performance improvements for SWAP, highlighting its potential in fields requiring complex decision-making and advancing LLM capabilities towards AGI.

Essay on "Deliberate Reasoning for LLMs as Structure-aware Planning with Accurate World Model"

The academic paper titled "Deliberate Reasoning for LLMs as Structure-aware Planning with Accurate World Model" presents a structured framework, named SWAP, to enhance the reasoning capabilities of LLMs. The authors address a fundamental issue in LLMs: their limited capacity for complex, multi-step decision-making. In contrast to human cognition, where structured reasoning is prevalent, LLMs often rely on intuition through token prediction, lacking an embedded reasoning mechanism.

Summary of the SWAP Framework

The SWAP framework integrates structured reasoning by introducing a multi-step planning approach informed by an accurate world model. Specifically, this framework diverges from traditional Chain-of-Thought (CoT) methodologies by integrating structural information, which guides the reasoning process with a world model. This structure provides a soft verification mechanism over the steps, targeting the accuracy of world state predictions through a Generator-Discriminator (G-D) architecture.

Technical Contributions

1. Structure-aware Planning: The paper presents entailment graphs as a core component of the structured reasoning process. These graphs clarify how given premises lead to intermediate conclusions and validate the final answers for coherence and logical verification.
2. Generator-Discriminator Architecture: The G-D architecture is central to improving the model’s reasoning performance. The generator predicts the subsequent state, while the discriminator ensures logical consistency in line with the problem context.
3. Diversity-based Modelling: SWAP addresses the bottlenecks in action and state generation by utilizing diversity-based modeling techniques that encourage the policy model to explore a wide range of potential actions. This ensures more reliable exploration and prevents premature convergence on suboptimal solutions.
4. Contrastive Ranking for Discrimination: The framework includes a contrastive ranking strategy that enhances discrimination accuracy by promoting rigorous comparison between possible candidate actions and states.

Experimental Evaluation

The robustness of the SWAP framework is substantiated through diverse reasoning-intensive benchmarks, including mathematical reasoning, logical reasoning, and coding tasks. The results demonstrate significant improvements over existing models, highlighting SWAP’s ability to achieve superior performance consistently.

Implications and Future Directions

The practical implications of this research are substantial, particularly in fields relying on complex and structured decision-making processes, such as scientific computations, strategic game planning, and advanced programming tasks. Theoretically, it advances the understanding of structured reasoning within LLMs, illustrating the potential of integrating structural and logical frameworks into existing token-based prediction systems.

Further exploration could lead to developments in adaptive reasoning strategies, where models interact dynamically with world models for continuous fine-tuning. There is also potential in combining SWAP with reinforcement learning techniques to optimize decision-making processes over extended reasoning tasks.

In conclusion, the SWAP framework marks a significant stride in elevating the cognitive emulation of LLMs. By embedding structure-aware planning and a robust world model, this research orchestrates an efficient navigation of complex reasoning landscapes, thus reinforcing the bridge toward achieving nuanced artificial general intelligence capabilities.