arXiv:2606.13316v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a central technique for improving long-horizon reasoning in Large Language Models (LLMs). However, existing RLVR methods often encourage unnecessarily long reasoning rollouts, which can degrade reasoning coherence and exhaust the available context budget. Existing approaches to long-context organization often depend on external mechanisms to organize rollouts, rather than enabling the model to manage its own reasoning trajectory. To address this limitation, we propose ReSum, a novel RLVR fr
The increasing complexity and length of AI reasoning tasks necessitate more efficient and coherent management of LLM operations, especially as context windows expand.
Improving LLM reasoning coherence and efficiency directly impacts the practical utility and scalability of AI agents, making their deployment more feasible and reliable.
This research outlines a method for LLMs to self-manage reasoning trajectories, reducing reliance on external mechanisms and potentially unlocking more sophisticated agentic behaviors.
- · AI developers
- · NLP researchers
- · Companies deploying AI agents
- · Less efficient LLM architectures
- · Developers reliant on manual prompt engineering
Improved performance and reduced computational cost for complex LLM-driven tasks.
Accelerated development and adoption of advanced AI agents capable of long-horizon planning.
Enhanced automation across white-collar workflows, leading to significant productivity gains.
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Read at arXiv cs.AI