VI-CuRL: Stabilizing Verifier-Independent RL Reasoning via Confidence-Guided Variance Reduction
arXiv:2602.12579v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a dominant paradigm for enhancing Large Language Models (LLMs) reasoning, yet its reliance on external verifiers limits its scalability. Recent findings suggest that RLVR primarily functions by eliciting latent capabilities, motivating the development of verifier-free algorithms. However, in such settings, standard methods like Group Relative Policy Optimization face a critical challenge: destructive gradient variance that often leads to training collapse. To address this is
The rapid advancement and adoption of Large Language Models (LLMs) are pushing researchers to find more scalable and autonomous reasoning methods, leading to verifier-free reinforcement learning approaches.
This research addresses a critical limitation in enhancing LLM reasoning, moving towards more self-sufficient and scalable AI systems, which impacts the future development and deployment of advanced AI.
The reliance on external verifiers for RL-enhanced LLMs may diminish, accelerating the development of more autonomous and generalized AI agents by overcoming current scalability bottlenecks.
- · AI research institutions
- · Companies developing LLMs
- · AI agent developers
- · Providers of external AI verifiers
- · Companies relying on less scalable RLVR
More robust and efficient training methods for advanced LLMs will emerge, leading to faster progress in AI capabilities.
The reduced need for human oversight in AI training could accelerate the deployment of autonomous AI systems across various industries.
The increased scalability of LLM reasoning could lead to a proliferation of complex AI agents that can operate with less human intervention, potentially disrupting white-collar workflows.
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Read at arXiv cs.LG