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This paper proposes a unified framework for personalized agentic reinforcement learning that decouples generic task rewards from personalized preference rewards, introducing PARPO and PSGM for preference-aligned policy optimization and skill retrieval.
This paper formalizes trust calibration for agentic tool use as a preference learning problem, using Gaussian processes and Bayesian optimization to decide when an AI agent's actions should be autonomous or require human approval.
Proposes AMATA, a multi-agent trajectory alignment framework for knowledge-intensive question answering that introduces intra-trajectory preference learning and inter-agent dependency learning to improve factual grounding and interpretability, outperforming baselines on five benchmarks.
This paper introduces the Hybrid Reward-Cyclic (HRC) model and Dynamic Self-Play Preference Optimization (DSPPO) to address the cyclic nature of human preferences in LLM alignment, achieving improved performance over Bradley-Terry and General Preference Model baselines.
This paper introduces CLIPR, a framework that learns transferable latent user preferences from minimal conversational input to improve human-aligned decision making in LLMs.
This paper introduces xi-DPO, a novel preference optimization method that reformulates the objective to minimize distance to optimal ratio reward margins, addressing hyperparameter tuning challenges in SimPO. Experimental results show that xi-DPO outperforms existing methods on open benchmarks.
WildFeedback is a novel framework that leverages in-situ user feedback from actual LLM conversations to automatically create preference datasets for aligning language models with human preferences, addressing scalability and bias issues in traditional annotation-based alignment methods.