Shattering the Autoregressive Curse: Dynamic Epistemic Entropy Orchestrated Erasable Reinforcement Learning for LLMs
arXiv:2606.17735v1 Announce Type: new Abstract: Although reinforcement learning (RL) has expanded the cognitive boundaries of large language models (LLMs), it often remains vulnerable to the autoregressive curse in long-horizon logical reasoning: small epistemic perturbations introduced early in generation can propagate irreversibly along the Markov decision process flow, triggering cascading failures that drive the reasoning trajectory toward collapse. To overcome this autoregressive cascade, in which a single early mistake can compromise all subsequent reasoning steps, we propose dynamic epi
The paper addresses fundamental limitations in LLM reasoning, particularly for long-horizon tasks, which is a significant bottleneck for AI agents. Research is rapidly advancing to overcome these inherent challenges.
Improving LLM reasoning robustness is critical for reliable AI deployment in complex applications, moving beyond current susceptibility to cascading errors. This breakthrough could unlock more capable and trustworthy autonomous systems.
The ability of LLMs to perform extended, logical reasoning with fewer errors due to early-stage perturbations is enhanced, leading to more resilient and reliable AI system outputs.
- · AI developers and researchers
- · Companies deploying AI agents
- · Autonomous system developers
- · Companies relying on less robust LLM architectures
- · Problem domains requiring flawless long-horizon reasoning
LLMs demonstrate significantly improved performance on complex, multi-step reasoning tasks.
The reliability of AI agents increases dramatically, enabling their deployment in more sensitive and critical applications.
White-collar workflow automation accelerates as AI agents can handle more nuanced and lengthy logical processes autonomously.
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Read at arXiv cs.AI