ALIVE: Awakening LLM Reasoning via Adversarial Learning and Instructive Verbal Evaluation
Yiwen Duan, Jing Ye, Xinpei Zhao
Why It Matters
What makes this one worth your time
This approach could lead to more autonomous and adaptable AI systems by reducing reliance on costly and brittle reward signals.
ALIVE enhances LLM reasoning by combining adversarial learning with verbal feedback to bypass traditional RL limitations.
Summary
The paper introduces ALIVE, a framework for improving reasoning in Large Language Models by integrating adversarial learning with instructive verbal feedback, aiming to overcome limitations of traditional reinforcement learning approaches.
Key contributions
- Proposes a new framework, ALIVE, for reasoning in LLMs.
- Demonstrates empirical improvements in reasoning tasks across multiple domains.
- Introduces the concept of 'Cognitive Synergy' for internalizing logic.
Notable insights
- The integration of adversarial learning with instructive verbal feedback as a novel method for reasoning enhancement.
- The concept of 'Cognitive Synergy' to unify problem posing, solving, and judging within a single model.
Possible limitations
- Not stated in the abstract
Abstract
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 Evaluation}), a hands-free alignment framework that moves beyond scalar reward optimization toward intrinsic reasoning acquisition. Grounded in the principle of \emph{Cognitive Synergy}, ALIVE unifies problem posing, solving, and judging within a single policy model to internalize the logic of correctness. By coupling adversarial learning with instructive verbal feedback, ALIVE enables models to internalize evaluative criteria directly from raw corpora, effectively transforming external critiques into an endogenous reasoning faculty. Empirical evaluations across mathematical reasoning, code generation, and general logical inference benchmarks demonstrate that ALIVE consistently mitigates reward signal limitations. With identical data and compute, it achieves accuracy gains, markedly improved cross-domain generalization, and higher self-correction rates. These results indicate that the reasoning trinity fosters a self-sustaining trajectory of capability growth, positioning ALIVE as a scalable foundation for general-purpose reasoning alignment without human-in-the-loop supervision.