← Search

Yichen You

2 accepted papers

2026

Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models

ICML 2026poster

Improving reasoning abilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications. Looped transformers address this by performing multiple latent iterations to refine each token beyond a single forward pass. However, we identify a latent ov…

Cited by 0SourceScholar
2025

R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing

NeurIPS 2025poster

Large Language Models (LLMs) achieve impressive reasoning capabilities at the cost of substantial inference overhead, posing substantial deployment challenges. Although distilled Small Language Models (SLMs) significantly enhance efficiency, their performance suffers as they fail to follow LLMs' rea…

Cited by 0SourcecodeScholar