Scaling test-time compute via long Chain-ofThought unlocks remarkable gains in reasoning capabilities, yet it faces practical limits due to the linear growth of KV cache and quadratic attention complexity. In this paper, we introduce Accordion-Thinking, an end-to-end framework where LLMs learn to self-regulate the granularity of the reasoning steps through dynamic summarization. This mechanism enables a Fold inference mode, where the model periodically summarizes its thought process and discards former thoughts to reduce dependency on historical tokens. We apply reinforcement learning to incentivize this capability further, uncovering a critical insight: the accuracy gap between the highly efficient Fold mode and the exhaustive Unfold mode progressively narrows and eventually vanishes over the course of training. This phenomenon demonstrates that the model learns to encode essential reasoning information into compact summaries, achieving effective compression of the reasoning context. Our Accordion-Thinker demonstrates that with learned self-compression, LLMs can tackle complex reasoning tasks with minimal dependency token overhead without compromising solution quality, and it achieves a 3x throughput while maintaining accuracy on a 48GB GPU memory configuration, while the structured step summaries provide a human-readable account of the reasoning process.

翻译：通过长链思维扩展测试时计算量能显著提升推理能力，但由于键值缓存线性增长和注意力复杂度二次方增长，该方法面临实际限制。本文提出手风琴式思维——一种端到端框架，使大语言模型通过动态摘要学习自调节推理步骤的粒度。该机制支持折叠推理模式：模型周期性地总结其思维过程并丢弃先前思考，从而减少对历史标记的依赖。我们应用强化学习进一步强化此能力，并发现关键现象：高效折叠模式与详尽展开模式之间的准确率差距在训练过程中逐渐缩小直至消失。该现象表明模型学会了将关键推理信息编码为紧凑摘要，实现了推理上下文的有效压缩。我们的手风琴思维模型证明，通过学习自压缩机制，大语言模型能以最小依赖标记开销处理复杂推理任务且不损失求解质量，在48GB GPU内存配置下保持精度的同时实现3倍吞吐量提升，而结构化步骤摘要则为推理过程提供了人类可读的记录。