arXiv:2602.05472v2 Announce Type: replace Abstract: The quest for expert-level reasoning in Large Language Models (LLMs) has been hampered by a persistent \textit{reward bottleneck}: traditional reinforcement learning (RL) relies on scalar rewards that are \textbf{costly} to scale, \textbf{brittle} across domains, and \textbf{blind} to the underlying logic of a solution. This reliance on external, impoverished signals prevents models from developing a deep, self-contained understanding of reasoning principles. We introduce \textbf{ALIVE} (\emph{Adversarial Learning with Instructive Verbal Eval
The persistent limitations of scalar reward functions in LLM training are becoming a critical bottleneck as researchers push for more advanced reasoning capabilities.
Achieving expert-level reasoning in LLMs is fundamental for autonomous AI systems, moving beyond pattern matching to true problem-solving.
Traditional reinforcement learning's reliance on simplistic rewards for LLMs is being challenged by advanced techniques that foster deeper, self-contained understanding.
- · AI research institutions
- · developers of advanced LLMs
- · sectors requiring complex automated reasoning
- · AI models reliant solely on scalar RL
- · companies without advanced AI research capabilities
The ALIVE methodology could significantly improve the reasoning, robustness, and interpretability of large language models.
Enhanced LLM reasoning might accelerate the development of more capable AI agents across various domains, automating complex cognitive tasks.
The ability of LLMs to self-evaluate and learn reasoning principles could lead to a 'meta-learning' paradigm where AI systems become highly adaptable and less reliant on human supervision for complex problem-solving.
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Read at arXiv cs.AI