most citedSPARK: Stepwise Process-Aware Rewards for Reference-Free Reinforcement Learning

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cs.AI2026

PReMISE: Policy Rubrics as Measurement Specifications for LLM Judges

Swastik Roy, Rajkumar Pujari, Tharindu Kumarage +5

LLM judges are increasingly used to evaluate open-ended responses, but their scores depend strongly on the rubrics that condition them. A vague rubric asking for a response to be `…

cs.AI2025

JudgeBoard: Benchmarking and Enhancing Small Language Models for Reasoning Evaluation

Zhenyu Bi, Gaurav Srivastava, Yang Li +4

While small language models (SLMs) have shown promise on various reasoning tasks, their ability to judge the correctness of answers remains unclear compared to large language model…

cs.AI2025

OPTAGENT: Optimizing Multi-Agent LLM Interactions Through Verbal Reinforcement Learning for Enhanced Reasoning

Zhenyu Bi, Meng Lu, Yang Li +4

Large Language Models (LLMs) have shown remarkable reasoning capabilities in mathematical and scientific tasks. To enhance complex reasoning, multi-agent systems have been proposed…