3 papers
cs.LG2025
Lookahead Unmasking Elicits Accurate Decoding in Diffusion Language Models
Sanghyun Lee, Seungryong Kim, Jongho Park +1
Masked Diffusion Models (MDMs) as language models generate by iteratively unmasking tokens, yet their performance crucially depends on the inference time order of unmasking. Prevai…
cs.LG2025
Effective Test-Time Scaling of Discrete Diffusion through Iterative Refinement
Sanghyun Lee, Sunwoo Kim, Seungryong Kim +2
Test-time scaling through reward-guided generation remains largely unexplored for discrete diffusion models despite its potential as a promising alternative. In this work, we intro…
cs.AI2025
Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess
Dongyoon Hwang, Hojoon Lee, Jaegul Choo +2
While reinforcement learning (RL) for large language models (LLMs) has shown promise in mathematical reasoning, strategic reasoning for LLMs using RL remains largely unexplored. We…