A new arXiv preprint describes a method for letting stakeholders define reward functions for reinforcement learning agents without needing to sample from the environment. The authors position the work as reducing the manual effort of reward design that preference-based approaches like online RLHF are meant to address. The same paper was listed in both the cs.AI and cs.LG announcement feeds.
A paper on arXiv compares five proper scoring rules used as training objectives for large language models making binary forecasts about real-world events. The author reports that the choice of reward function influences both the accuracy and the behavior of the resulting forecasters, even though the rules are theoretically equivalent. The work suggests reward design matters when fine-tuning models for prediction tasks.
A new arXiv paper presents SocialRL, a framework that applies multi-turn reinforcement learning together with carefully designed rewards to sharpen how language models handle social context in extended conversations. The work targets agents' capacity to read situational cues, infer speaker intent, and adjust behavior over sustained dialogue, with the goal of more effective and trustworthy human-AI collaboration.