Title: Bingo: Boosting Efficient Reasoning of LLMs via Dynamic and Significance-based Reinforcement Learning
Primary area: reinforcement learning
Keywords: Reasoning, Efficient Reasoning, Question Answering, Large Language Models

Abstract: Large language models have demonstrated impressive reasoning capabilities, yet they often suffer from inefficiencies due to unnecessarily verbose or redundant outputs. While many works have explored reinforcement learning (RL) to enhance reasoning abilities, most primarily focus on improving accuracy, with limited attention to reasoning efficiency. Some existing approaches introduce direct length-based rewards to encourage brevity, but this often leads to noticeable drops in accuracy. In this paper, we propose Bingo, an RL framework that advances length-based reward design to boost efficient reasoning. Bingo incorporates two key mechanisms: a significance-aware length reward, which gradually guides the model to reduce only insignificant tokens, and a dynamic length reward, which initially encourages elaborate reasoning for difficult questions but decays over time to improve overall efficiency. Experiments across multiple reasoning benchmarks show that Bingo improves both accuracy and efficiency. It outperforms the vanilla reward and several other length-based reward baselines in RL, achieving a favorable trade-off between accuracy and efficiency. These results underscore the potential of training LLMs explicitly for efficient reasoning.

TL;DR: Bingo is a reinforcement learning framework that trains LLMs for efficient reasoning by combining significance-aware and dynamic length rewards, improving both accuracy and efficiency across reasoning benchmarks.