arXiv:2602.06960v3 Announce Type: replace Abstract: Large reasoning models achieve strong performance by scaling inference-time chain-of-thought, but this paradigm suffers from quadratic cost, context length limits, and degraded reasoning due to lost-in-the-middle effects. Iterative reasoning mitigates these issues by periodically summarizing intermediate thoughts, yet existing methods rely on supervised learning or fixed heuristics and fail to optimize when to summarize, what to preserve, and how to resume reasoning. We propose InftyThink+, an end-to-end reinforcement learning framework that
The continuous scaling of large language models is hitting practical limits regarding cost, context, and reasoning quality, pushing researchers to seek more efficient inference-time paradigms.
This research addresses fundamental constraints in large reasoning models, promising more efficient and effective AI agents crucial for advanced applications.
The development of reinforcement learning frameworks for iterative reasoning could significantly reduce the computational burden and improve long-term coherence in AI, making complex tasks more feasible.
- · AI developers
- · Cloud computing providers (from increased efficiency)
- · Enterprises adopting AI agents
- · Inefficient large model architectures
- · Organizations reliant on current high-cost inference paradigms
Improved efficiency and capability of AI reasoning in complex, multi-step tasks.
Acceleration of the development and deployment of sophisticated AI agents across various industries.
Enhanced automation of white-collar workflows, leading to shifts in labor markets and increased productivity.
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Read at arXiv cs.CL