arXiv:2606.09883v1 Announce Type: new Abstract: Large language models (LLMs) have made remarkable progress in reasoning tasks, largely driven by post-training paradigms, especially reinforcement learning with verifiable rewards (RLVR). However, a critical bottleneck persists: RLVR fails on highly challenging zero-reward problems, where all sampled reasoning trajectories yield uniformly failed outcomes, providing no optimization signal to drive model improvement. Prior efforts to address this limitation, such as dense process supervision, partial reward assignment, or prefix-guided exploration,
The rapid advancement of LLMs has exposed the limitations of current training paradigms, especially in complex reasoning where immediate rewards are scarce, necessitating new approaches to unlock further progress.
Overcoming zero-reward problems in LLMs is crucial for developing more robust and autonomously reasoning AI, which will expand their capabilities beyond current limitations into more sophisticated and open-ended tasks.
This research introduces a method to enable LLMs to learn from problems where traditional reward systems fail, potentially accelerating the development of highly capable AI agents and complex autonomous systems.
- · AI research labs
- · Developers of autonomous AI agents
- · Hardware manufacturers for AI (long-term)
- · Companies reliant on simple heuristics for AI training
- · Current reinforcement learning paradigms without adaptation
Improved reasoning capabilities in large language models leading to more coherent and effective AI behaviors.
Accelerated development of sophisticated AI agents capable of tackling complex, multi-step problems with delayed or non-existent direct reward signals.
Broader deployment of AI in critical sectors requiring advanced autonomous problem-solving, potentially leading to new economic efficiencies and disruptions.
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Read at arXiv cs.LG