RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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Give Your AI a Hint: LLMs Learn to Brainstorm Better Strategies for Tricky Problems!

This paper introduces RLAD, a novel two-player reinforcement learning framework that trains large language models (LLMs) to discover and utilize "reasoning abstractions"—concise natural language descriptions of procedural and factual knowledge. This approach enables more structured exploration and diverse problem-solving strategies, leading to significant improvements in LLM performance on math reasoning and other tasks. The authors demonstrate that prioritizing the generation of diverse abstractions over merely scaling solution generation is more effective for performance gains.

Possible Conflicts of Interest

Funding acknowledged from Schmidt Sciences, DSAI cluster, FLAME cluster at CMU, Delta, Foundry, NSF Graduate Research Fellowship Program, Toyota Research Institute (TRI), Amazon gift, and the Office of Naval Research. While these are common sources of academic research funding, TRI and Amazon are companies with vested interests in AI technology advancements. This represents an indirect conflict of interest, but no direct product endorsement or regulation is apparent.

Identified Weaknesses

Limited Scope of Evaluation

While the paper makes broad claims about improving reasoning capabilities, the primary evaluation focuses heavily on mathematical reasoning benchmarks. Although other domains (healthcare, legal, web security) are mentioned, the empirical evidence for generalizability across diverse 'reasoning problems' is less detailed, potentially overlooking unique challenges in those areas.

Reliance on Stronger Models for Warmstarting

The initial set of high-quality reasoning abstractions used to warmstart the abstraction generator is synthetically created by prompting a *stronger* reasoning model (o4-mini). This implies an initial dependency on external, more capable models for generating good abstractions, which could be a practical limitation for bootstrapping in new or under-explored domains where such stronger models are not readily available or perform suboptimally.

Challenges with Naïve Reward Design

The paper explicitly discusses inherent challenges in the two-player RL setup, such as the abstraction generator learning to leak answers, the solution generator ignoring abstractions, or imbalances between the generators drowning out the learning signal. While a modified reward system is proposed, these issues highlight the delicate nature of aligning incentives and the potential difficulty in ensuring robust fine-tuning across various problem types.

Computational Cost

Training two large language models using reinforcement learning is computationally intensive. The paper's discussion of 'scaling test-time compute' and 'compute tradeoffs' underscores that efficient resource allocation is critical, making this approach potentially costly and resource-demanding, which could limit its practical adoption and scalability for smaller research teams or real-world deployment scenarios.

Opaque Interpretability of Abstraction Discovery

Although abstractions are qualitatively categorized, the paper states that the process of generating them is not hand-engineered for interpretability, and interpretations are 'specific to an individual problem' rather than representative of the discovery process itself. This limits deeper understanding of *how* the model identifies and frames useful abstractions, hindering potential human-guided improvements in abstraction quality beyond empirical observation.

Rating Explanation

The paper presents a novel and effective two-player reinforcement learning framework (RLAD) that significantly improves LLM reasoning performance through abstraction discovery. The methodology is well-described, and results show consistent gains across various benchmarks. While the primary evaluation is focused on math reasoning and the initial abstraction generation relies on stronger models, these are minor limitations for a strong and innovative contribution to the field of AI reasoning.

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