Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning
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Computer Science > Computation and Language
arXiv:2605.20201 (cs)
[Submitted on 6 Apr 2026]
Title:Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning
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Abstract:Recent large language models support inputs of up to 10 million tokens, yet they perform poorly on long-context tasks that require complex reasoning. Such tasks can be solved using only a subset of the input -- a proxy context -- rather than the full sequence. Despite sharing the same underlying reasoning process, models exhibit a significant performance disparity between proxy and full contexts. To improve long-context reasoning, we propose ProxyCoT, a novel training framework that transfers reasoning capabilities from short proxy contexts to full long contexts. Specifically, we first obtain high-quality chain-of-thought reasoning traces on proxy contexts through reinforcement learning or distillation from a larger teacher model, and then ground the generated traces in full long contexts with supervised fine-tuning. Experiments across different datasets demonstrate that ProxyCoT consistently outperforms strong baselines with reduced computational overhead. Furthermore, models trained with ProxyCoT generalize their long-context reasoning capabilities to out-of-domain tasks.
| Comments: | Long, ACL 2026 (Main conference) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.20201 [cs.CL] |
| (or arXiv:2605.20201v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20201
arXiv-issued DOI via DataCite
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View a PDF of the paper titled Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning, by Miao Li and 3 other authors
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