3 papers
cs.LG2026
RL2ML: Finite-Rollout Surrogate Objectives from Reinforcement Learning to Maximum Likelihood
Yifu Zheng
Correctness-based Reinforcement Learning with Verifiable Rewards (RLVR) trains language models from binary feedback on sampled outputs, but the objective optimized in expectation a…
cs.AI2026
Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models
Qi Liu, Mingdi Sun, Yongyi He +5
Supervised fine-tuning (SFT) followed by reinforcement learning (RL) has become a standard post-training paradigm for large language models. This paradigm provides a cold-start for…
cs.CV2025
Half-order Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer
Tao Ren, Zishi Zhang, Jingyang Jiang +9
The probabilistic diffusion model (DM), generating content by inferencing through a recursive chain structure, has emerged as a powerful framework for visual generation. After pre-…