ALIVE Framework Enhances LLM Reasoning via Adversarial Learning
A new alignment framework called ALIVE (Adversarial Learning with Instructive Verbal Evaluation) has been developed by researchers to address the reward bottleneck encountered in large language models. Conventional reinforcement learning depends on scalar rewards, which can be expensive, fragile, and overlook the logic behind solutions. ALIVE integrates problem formulation, resolution, and evaluation into one policy model, facilitating the development of intrinsic reasoning through cognitive collaboration. By employing adversarial learning alongside instructive verbal feedback, this framework allows models to grasp correctness logic independently of external reward signals. This innovative method shifts focus from scalar reward optimization to enhanced reasoning abilities. The research paper can be found on arXiv with the identifier 2602.05472.
Key facts
- ALIVE stands for Adversarial Learning with Instructive Verbal Evaluation
- It addresses the reward bottleneck in LLM reasoning
- Traditional RL uses scalar rewards that are costly, brittle, and logic-blind
- ALIVE unifies problem posing, solving, and judging in one policy model
- It is grounded in the principle of Cognitive Synergy
- The framework uses adversarial learning with instructive verbal feedback
- It enables models to internalize the logic of correctness
- The paper is published on arXiv with ID 2602.05472
Entities
Institutions
- arXiv