Breadcrumbs Reasoning: Memory-Efficient Reasoning with Compression Beacons

• URL: http://arxiv.org/abs/2510.13797v2
• Date: Mon, 10 Nov 2025 00:06:46 GMT
• Title: Breadcrumbs Reasoning: Memory-Efficient Reasoning with Compression Beacons
• Authors: Giovanni Monea, Yair Feldman, Shankar Padmanabhan, Kianté Brantley, Yoav Artzi,
• Abstract summary: We propose to periodically compress the generation KV cache with a learned, special-purpose token.<n>We train the model to perform this compression via a modified joint distillation and reinforcement learning framework.<n>Our method achieves a superior memory-accuracy frontier compared to both the model without cache compression and training-free compression techniques.
• Score: 22.085345397844687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The scalability of large language models for long-context reasoning is severely constrained by the linear growth of their Transformer key-value cache, which incurs significant memory and computational costs. We posit that as a model generates reasoning tokens, the informational value of past generated tokens diminishes, creating an opportunity for compression. In this work, we propose to periodically compress the generation KV cache with a learned, special-purpose token and evict compressed entries. We train the model to perform this compression via a modified joint distillation and reinforcement learning (RL) framework. Our training method minimizes overhead over the conventional RL process, as it leverages RL outputs for distillation. Empirically, our method achieves a superior memory-accuracy Pareto frontier compared to both the model without cache compression and training-free compression techniques.

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