1 citations · 2 across the 4 of their papers we have counts for
5 papers
CanvasAnneal: Curriculum Reinforcement Learning for Diffusion Language Models
Blake Olson, Yuhang Song, Emmett McQuinn +1
Diffusion Language Models (DLMs) offer promising parallel generation capabilities but lag behind autoregressive models in complex reasoning and tool-use tasks. While Reinforcement…
Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning
Shubham Parashar, Shurui Gui, Xiner Li +8
We aim to improve the reasoning capabilities of language models via reinforcement learning (RL). Recent RL post-trained models like DeepSeek-R1 have demonstrated reasoning abilitie…
Complex LLM Planning via Automated Heuristics Discovery
Hongyi Ling, Shubham Parashar, Sambhav Khurana +6
We consider enhancing large language models (LLMs) for complex planning tasks. While existing methods allow LLMs to explore intermediate steps to make plans, they either depend on…
Inference-Time Computations for LLM Reasoning and Planning: A Benchmark and Insights
Shubham Parashar, Blake Olson, Sambhav Khurana +4
We examine the reasoning and planning capabilities of large language models (LLMs) in solving complex tasks. Recent advances in inference-time techniques demonstrate the potential…
Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models
Cong Fu, Xiner Li, Blake Olson +2
Structure-based drug design (SBDD) is crucial for developing specific and effective therapeutics against protein targets but remains challenging due to complex protein-ligand inter…