41 citations · 60 across the 25 of their papers we have counts for
4 papers · 1 filter
GPU Forecasters: Language Models as Selective Surrogates for Kernel Runtime Optimization
Zaid Khan, Justin Chih-Yao Chen, Jaemin Cho +2
GPU kernels are the workhorse of modern deep learning, and optimizing them (via evolutionary search or coding agents) usually requires repeated measurement on target hardware. Whil…
Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems
Justin Chih-Yao Chen, Archiki Prasad, Zaid Khan +4
Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning abilities of LLMs, yet a fundamental limitation remains: models cannot learn from problems that are…
Think Right: Learning to Mitigate Under-Over Thinking via Adaptive, Attentive Compression
Joykirat Singh, Justin Chih-Yao Chen, Archiki Prasad +3
Recent thinking models are capable of solving complex reasoning tasks by scaling test-time compute, but this scaling should be allocated in line with task difficulty. On one hand,…
Nudging the Boundaries of LLM Reasoning
Justin Chih-Yao Chen, Becky Xiangyu Peng, Prafulla Kumar Choubey +4
Current online reinforcement learning (RL) algorithms like GRPO share a key limitation in LLM reasoning: they cannot learn from problems that are "unsolvable" to the model. In othe…