Tag
Introduces SP3O, a novel reward-model-free, critic-free, gradient-based preference-based RL algorithm that leverages segment-level preferences, demonstrating improved performance in robotic control and LLM fine-tuning, especially for long-horizon tasks.
This paper introduces LEMUR, a framework that combines multi-objective reinforcement learning with preference-based learning from multiple human feedback to learn Pareto-optimal policies without predefined reward functions.
S2T-RLHF proposes a sentence-to-token reward decomposition framework that improves training stability and robustness in preference-based RLHF by assigning sequence-level rewards at sentence granularity, avoiding the instability of overly fine-grained token-level refinement.