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[2503.06692] InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models
Summary
InftyThink presents a novel approach to long-context reasoning in large language models, addressing computational limits and enhancing performance through iterative summarization.
Why It Matters
This research is significant as it tackles the critical limitations of current long-context reasoning methods in large language models, which are essential for advanced AI applications. By proposing a scalable and efficient paradigm, InftyThink could lead to more capable AI systems that can handle complex reasoning tasks without the constraints of traditional architectures.
Key Takeaways
- InftyThink transforms long-context reasoning into an iterative process.
- The new paradigm significantly reduces computational costs while improving performance.
- Experiments show 3-11% performance improvements on key benchmarks.
- The approach challenges the trade-off between reasoning depth and computational efficiency.
- A methodology for reconstructing long-context datasets into iterative formats is introduced.
Computer Science > Computation and Language arXiv:2503.06692 (cs) [Submitted on 9 Mar 2025 (v1), last revised 25 Feb 2026 (this version, v5)] Title:InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models Authors:Yuchen Yan, Yongliang Shen, Yang Liu, Jin Jiang, Mengdi Zhang, Jian Shao, Yueting Zhuang View a PDF of the paper titled InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models, by Yuchen Yan and 6 other authors View PDF HTML (experimental) Abstract:Advanced reasoning in large language models has achieved remarkable performance on challenging tasks, but the prevailing long-context reasoning paradigm faces critical limitations: quadratic computational scaling with sequence length, reasoning constrained by maximum context boundaries, and performance degradation beyond pre-training context windows. Existing approaches primarily compress reasoning chains without addressing the fundamental scaling problem. To overcome these challenges, we introduce InftyThink, a paradigm that transforms monolithic reasoning into an iterative process with intermediate summarization. By interleaving short reasoning segments with concise progress summaries, our approach enables unbounded reasoning depth while maintaining bounded computational costs. This creates a characteristic sawtooth memory pattern that significantly reduces computational complexity compared to traditional approaches. Furthermore, we develop a methodolo...
