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REAL: Regression-Aware Reinforcement Learning for LLM-as-a-Judge
Yasi Zhang, Tianyu Chen, Mingyuan Zhou +3
Large language models (LLMs) are increasingly deployed as automated evaluators that assign numeric scores to model outputs, a paradigm known as LLM-as-a-Judge. However, standard Re…
Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay
Yifan Sun, Jingyan Shen, Yibin Wang +4
Reinforcement learning (RL) has become an effective approach for fine-tuning large language models (LLMs), particularly to enhance their reasoning capabilities. However, RL fine-tu…
A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation
Xinran Song, Tianyu Chen, Mingyuan Zhou
Estimating individualized treatment effects from observational data is a central challenge in causal inference, largely due to covariate imbalance and confounding bias from non-ran…
Score Distillation Beyond Acceleration: Generative Modeling from Corrupted Data
Yasi Zhang, Tianyu Chen, Zhendong Wang +3
Learning generative models directly from corrupted observations is a long standing challenge across natural and scientific domains. We introduce Restoration Score Distillation (RSD…
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation
Tianyu Chen, Yasi Zhang, Zhendong Wang +3
Diffusion models have achieved remarkable success in generating high-resolution, realistic images across diverse natural distributions. However, their performance heavily relies on…
Diffusion Policies creating a Trust Region for Offline Reinforcement Learning
Tianyu Chen, Zhendong Wang, Mingyuan Zhou
Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies. Diffusion Q-Learning (DQL), introducing diffusion models as a powerful and expressiv…