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20242026
most citedCurriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning

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cs.CL2026

Data-Efficient Autoregressive-to-Diffusion Language Models via On-Policy Distillation

Xingyu Su, Jacob Helwig, Shubham Parashar +6

We study the transformation of autoregressive models (ARLMs) into diffusion language models (DLMs). Rather than pretraining from scratch, prior work replaces the causal attention i…

cs.CL2026

Learnability-Informed Fine-Tuning of Diffusion Language Models

Shubham Parashar, Atharv Chagi, Jacob Helwig +5

We aim to improve the reasoning capabilities of diffusion language models (DLMs). While SFT is a popular post-training recipe for autoregressive models, its use in DLMs faces chall…