arXiv:2606.19771v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced Large Language Model (LLM) reasoning; however, it faces a fundamental optimization instability: uniform token updates precipitate entropy collapse, leading to premature convergence to suboptimal strategies, whereas excessive Shannon Entropy maximization can cause entropy explosion, driving blind exploration toward incoherent reasoning chains. To resolve this dichotomy, we introduce the Independent Combinatorial Tokens (ICT) framework, which shifts the optimization fo
The continuous advancements in LLM development and the increasing challenges in optimizing their reasoning capabilities necessitate new frameworks to overcome current limitations.
This development proposes a solution to fundamental optimization instabilities in LLM training, potentially leading to more stable, efficient, and robust AI reasoning systems.
The approach to optimizing LLM reasoning might shift from purely entropy-based methods to a more nuanced token-level distributional deviation analysis, allowing for better control over exploration and exploitation.
- · AI developers
- · LLM research institutions
- · Companies deploying advanced AI
- · Researchers in reinforcement learning
- · Developers reliant on suboptimal RL structures
- · LLMs prone to entropy collapse/explosion
Improved performance and stability of LLMs, reducing time and computational resources for training.
Faster development cycles for creating more sophisticated and reliable AI agents and applications.
The acceleration of complex problem-solving capabilities across various sectors due to more effective AI reasoning.
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Read at arXiv cs.AI