Dynamic Long Context Reasoning over Compressed Memory via End-to-End Reinforcement Learning

Zhuoen Chen, liu, Meishan Zhang, Baotian Hu, Min Zhang

Dynamic Long Context Reasoning over Compressed Memory via End-to-End Reinforcement Learning: 11 upvotes on Hugging Face Daily Papers, #27 of 57 papers on 2026-02-11. Day-by-day upvote history.

Large Language Models (LLMs) face significant challenges in long-context processing, including quadratic computational costs, information forgetting, and the context fragmentation inherent in retrieval-augmented generation (RAG). We propose a cognitively inspired framework for efficient long-context inference based on chunk-wise compression and selective memory recall, rather than processing all raw tokens. The framework segments long inputs into chunks and encodes each chunk into compressed memory representations using a learned compressor. A gating module dynamically selects relevant memory blocks, which are then iteratively processed by a reasoning module with an evolving working memory to solve downstream tasks. The compressor and reasoner are jointly optimized via end-to-end reinforcement learning, while the gating module is trained separately as a classifier. Experimental results show that the proposed method achieves competitive accuracy on multi-hop reasoning benchmarks such as RULER-HQA, extrapolates context length from 7K to 1.75M tokens, and offers a favorable accuracy-efficiency trade-off compared to strong long-context baselines. In particular, it achieves up to a 2 times reduction in peak GPU memory usage and a 6 times inference speedup over MemAgent.

Paper page on Hugging Face · arXiv

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