SCoRe uses multi-turn online RL with regularization on self-generated traces to improve LLM self-correction, achieving 15.6% and 9.1% gains on MATH and HumanEval for Gemini models.
Conservative Q-Learning for Offline Reinforcement Learning , url =
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DiPS uses a trained critic to select persuasion policies via Q-learning in a fire-rescue evacuation task and reports higher success rates than zero-shot LLM or RAG baselines in both simulation and human trials.
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Training Language Models to Self-Correct via Reinforcement Learning
SCoRe uses multi-turn online RL with regularization on self-generated traces to improve LLM self-correction, achieving 15.6% and 9.1% gains on MATH and HumanEval for Gemini models.
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DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents
DiPS uses a trained critic to select persuasion policies via Q-learning in a fire-rescue evacuation task and reports higher success rates than zero-shot LLM or RAG baselines in both simulation and human trials.