Bowen Yu

With the rapid advancement of Large Language Models (LLMs), developing effective critic modules for precise guidance has become crucial yet challenging. An ideal critic should both judge accurately and drive refinement, yet existing approaches focus primarily on judgment, leaving the capacity to guide refinement underexplored. To address this gap, we propose RefCritic, a long-chain-of-thought critic trained via reinforcement learning with dual rule-based rewards: (1) instance-level correctness of solution judgments and (2) gains of policy-model refinements driven by the critique, encouraging evaluations that are both reliable and actionable. Extensive experiments on Qwen2.5-14B-Instruct and DeepSeek-R1-Distill-Qwen-14B demonstrate that RefCritic achieves high judgment accuracy and surpasses step-level supervised baselines on fine-grained error localization with only solution-level supervision. Moreover, RefCritic produces meaningful evaluations that substantially benefit refinement, achieving +6.8\% and +9.3\% gains on AIME24 and AIME25 after one round of critique-refinement, and exhibits even stronger gains +13\% and +10\% as the computational cost scales up, indicating seamless adaptability to test-time strategies. RefCritic establishes that optimizing critics for refinement effectiveness, rather than solely for judgment, can bridge the gap between evaluation quality and practical utility.

Inspired by the success of scaling language modeling through massive real-world corpora, we demonstrate that similar power-law patterns exist in preference modeling. We propose \textbf{World Preference Modeling (WorldPM)}, which model a robust consensus on intrinsic utility from real-world user interactions. We curate a 15M-scale preference dataset from online forum data and conduct large-scale experiments on models from 1.5B to 72B parameters. Across 10 objective preference test sets with consensus-based labels, we observe two distinct scaling patterns: (1) common-sense preference (e.g., identifying factual errors) exhibits stable log-linear improvement across all model sizes, while (2) domain preference (e.g., coding, math, knowledge QA) reveals an emergent phenomenon where only sufficiently large models achieve meaningful scaling. Through training dynamics analysis, we reveal the learning mechanism behind preference scaling: the model first learns to discriminate using surface preferences, but counter-surface examples progressively compel it to discover intrinsic utility beyond surface features as training scales. Further experiments validate WorldPM as an effective foundation for preference fine-tuning, broadly improving generalization across human preference datasets of varying sizes (7K, 100K, and 800K), with gains exceeding 5\% on many key subtasks.