aiqbench analysis
This paper is squarely about AI: RL-based training of LLMs for long-context reasoning. Among the categories, it fits best under Machine Learning, since its contribution is a training method (a dense, information-gain-based reward and a Think-and-Read interleaving scheme) rather than a product, policy, or safety study. The reported gains (over 9% on LongBench v2, plus improvements on RULER and InfiniteBench) come from the authors' own benchmarks and should be checked for reproducibility, but the core takeaway is that denser, non-outcome-only reward signals can help models navigate very long inputs more effectively.
LongR: Unleashing Long-Context Reasoning via Reinforcement Learning with Dense Utility Rewards
arXiv:2602.05758v2 Announce Type: replace Abstract: Reinforcement Learning (RL) has emerged as a key driver for LLM reasoning. This capability is equally pivotal in long-context scenarios---such as long-dialogue understanding and structured data analysis---where the challenge extends beyond consuming tokens to performing rigorous deduction. While existing efforts focus on data synthesis or architectural changes, recent work points out that relying solely on sparse, outcome-only rewards yields limited gains, as such coarse signals are often insufficient to effectively guide the complex long-context reasoning. To address this, we propose LongR, a unified framework that enhances long-context performance by integrating a dynamic ``Think-and-Read'' mechanism---which interleaves reasoning with document consultation---with a contextual density reward based on relative information gain to quantify the utility of the relevant documents, without evidence annotations. Empirically, LongR achieves a gain of over 9\% on LongBench v2 and consistent improvements on RULER and InfiniteBench, demonstrating robust efficiency in navigating extensive contexts. Furthermore, LongR consistently enhances performance across diverse RL algorithms (e.g., DAPO, GSPO). Finally, we conduct in-depth analyses to investigate the impact of reasoning chain length on efficiency and the model's robustness against distractors.
Abstract:Reinforcement Learning (RL) has emerged as a key driver for LLM reasoning. This capability is equally pivotal in long-context scenarios---such as long-dialogue understanding and structured data analysis---where the challenge extends beyond consuming tokens to performing rigorous deduction. While existing efforts focus on data synthesis or architectural changes, recent work points out that relying solely on sparse, outcome-only rewards yields limited gains, as such coarse signals are often insufficient to effectively guide the complex long-context reasoning. To address this, we propose LongR, a unified framework that enhances long-context performance by integrating a dynamic ``Think-and-Read'' mechanism---which interleaves reasoning with document consultation---with a contextual density reward based on relative information gain to quantify the utility of the relevant documents, without evidence annotations. Empirically, LongR achieves a gain of over 9\% on LongBench v2 and consistent improvements on RULER and InfiniteBench, demonstrating robust efficiency in navigating extensive contexts. Furthermore, LongR consistently enhances performance across diverse RL algorithms (e.g., DAPO, GSPO). Finally, we conduct in-depth analyses to investigate the impact of reasoning chain length on efficiency and the model's robustness against distractors.
Computation and Language (cs.CL)
arXiv:2602.05758 [cs.CL]
(or arXiv:2602.05758v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2602.05758 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 270 words
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