Title: Revisiting LLM Reasoning via Information Bottleneck
Primary area: foundation or frontier models, including LLMs
Keywords: LLM Reasoning, RL Post-training, RL with verifiable rewards

Abstract: Large language models (LLMs) have recently demonstrated remarkable progress in reasoning capabilities through reinforcement learning with verifiable rewards (RLVR). By leveraging simple rule-based rewards, RL effectively incentivizes LLMs to produce extended chain-of-thought (CoT) reasoning trajectories, progressively guiding them toward correct answers. However, existing approaches remain largely heuristic and intuition-driven, limiting the development of principled methodologies. In this paper, we present a theoretical characterization of LLM reasoning grounded in information bottleneck (IB) principle, introducing \textit{IB-aware reasoning optimization} (IBRO), a framework that encourages reasoning trajectories to be both \textit{informative} about the final correct answer and \textit{generalizable} across diverse prompts. We derive a practical token-level surrogate objective and propose an efficient approximation, resulting in the lightweight \textit{IB regularization} method. This technique integrates seamlessly into existing RL-based post-training frameworks without additional computational overhead, requiring only a one-line code modification. Empirically, we validate IB regularization across multiple mathematical reasoning benchmarks and RL algorithms, demonstrating consistent improvements in LLM reasoning performance.