Скачать или смотреть RLAC: Adversarial Critic RL for LLM Generation

In this AI Research Roundup episode, Alex discusses the paper:
'RLAC: Reinforcement Learning with Adversarial Critic for Free-Form Generation Tasks(2511.01758v1)'
This work introduces RLAC, a post-training method where an LLM critic dynamically flags likely failure modes in free-form generation, which are then verified by an external validator. By jointly training the generator and adversarial critic, the approach reduces verification costs while improving output quality. Experiments show gains in factual accuracy for text and correctness for code, outperforming exhaustive verification and reward-model baselines. The results highlight the effectiveness of dynamic critics over fixed ones for scalable RL post-training.
Paper URL: https://arxiv.org/pdf/2511.01758

#AI #MachineLearning #DeepLearning #ReinforcementLearning #LLM #TextGeneration #CodeGeneration #PostTraining