ByteDance Researchers Introduce ProtoReasoning: Enhancing LLM Generalization via Logic-Based Prototypes

Understanding the Target Audience

The target audience for the ProtoReasoning framework includes AI researchers, business managers in tech, and data scientists interested in the application of large language models (LLMs) in various domains. They typically face challenges in achieving effective model generalization across different tasks and domains. Their goals include improving LLM performance, fostering innovation in AI applications, and enhancing problem-solving capabilities. They are interested in empirical research findings, practical applications of AI, and advancements in machine learning. Communication preferences lean towards technical documentation, research papers, and concise, informative articles.

Importance of Cross-Domain Reasoning in LLMs

Recent breakthroughs in LLMs, especially those utilizing Long Chain-of-Thought (CoT) techniques, demonstrate impressive generalization abilities across diverse domains. For instance, models trained on math or coding tasks often excel in unrelated areas, such as logical puzzles or creative writing. This flexibility is thought to arise from the models learning core reasoning patterns, or abstract reasoning prototypes, that facilitate broader transfer across tasks, allowing models to focus on the cognitive processes necessary for problem-solving rather than solely the problem presentation.

Evolution of Reasoning Approaches in LLMs

The approach to reasoning in large language models has transitioned from simple Chain-of-Thought and supervised fine-tuning techniques towards Reinforcement Learning (RL) methods. Models like DeepSeek-R1 and Seed-Thinking-v1.5 have significantly improved Long CoT reasoning by tackling mathematical problems, logic tasks, and coding challenges using RL techniques. These models benefit from rewards guided by accuracy metrics from ground-truth answers, enabling them to explore complex reasoning pathways while learning from errors and refining solutions iteratively. The novel introduction of “reasoning prototypes” enhances the understanding of core thinking patterns that empower models to generalize effectively across vastly different domains.

ProtoReasoning Framework: A Structured Approach

The ProtoReasoning framework, developed by ByteDance Seed in collaboration with Shanghai Jiao Tong University, enhances LLM reasoning capabilities through structured prototype representations like Prolog for logic and PDDL for planning. This system features an automated pipeline for translating problems into these formats, a verification setup utilizing interpreters, and a scalable problem synthesis process that negates the need for manual labeling. Models trained within this structured framework exhibited significant improvements across various tasks, yielding increases of +4.7% in logical reasoning, +6.3% in planning, +4.0% in general reasoning, and +1.0% in mathematical tasks. Importantly, training within this “prototype space” supports better generalization across similar tasks, validating the hypothesis that abstract reasoning patterns bolster cross-domain performance.

Prototype Constructor and Verification System

The architecture of the ProtoReasoning framework comprises two principal modules: a Prototype Constructor and a Verification System. The Prototype Constructor translates natural language problems into formal representations, while the Verification System assesses the correctness of solutions. In the case of Prolog, a systematic four-step pipeline generates a variety of logic problems, which are subsequently verified using SWI-Prolog. For planning tasks, operations such as plan generation and completion are executed using PDDL, with correctness ensured through the VAL validator. The training methodology encompasses teacher model distillation for reasoning pathways, along with difficulty-based sampling and filtering to guarantee that only high-quality data contributes to the fine-tuning of the model for robust generalization.

Evaluations and Measurable Improvements

The ProtoReasoning framework underwent rigorous evaluation through experiments utilizing a highly parameterized (150B) Mixture-of-Experts model (15B active), trained on a carefully curated dataset of Prolog and PDDL samples. The outcomes demonstrated consistent enhancements in logical reasoning, planning, and overall benchmark performance in metrics like MMLU and AIME 2024. A comprehensive ablation study contrasted Prolog-based training with natural language (NL) versions on aligned datasets, revealing significant advantages for both formats over the baseline, with Prolog achieving performance levels nearly indistinguishable from NL. This affirms that structured prototype training is applicable to natural language tasks; however, explicit reasoning—like Chain-of-Thought—proves critical, as low-sample categories exhibited weaker performance due to limited data availability.

Conclusions and Future Directions

In summary, ProtoReasoning establishes that abstract reasoning prototypes, exemplified through Prolog and PDDL, enable LLMs to generalize effectively across domains. Training models on these structured representations led to notable advancements in logical reasoning, planning, and general problem-solving capabilities. The results corroborate the hypothesis that shared reasoning patterns across domains facilitate knowledge transfer in models. While the empirical findings are promising, the precise nature of reasoning prototypes remains a subject for theoretical exploration. Future research will focus on formalizing these concepts mathematically and validating the findings through open-source models and datasets.

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