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cs.AI2025
Rethinking Reward Models for Multi-Domain Test-Time Scaling
Dong Bok Lee, Seanie Lee, Sangwoo Park +12
The reliability of large language models (LLMs) during test-time scaling is often assessed with \emph{external verifiers} or \emph{reward models} that distinguish correct reasoning…
cs.LG2025
Delta Attention: Fast and Accurate Sparse Attention Inference by Delta Correction
Jeffrey Willette, Heejun Lee, Sung Ju Hwang
The attention mechanism of a transformer has a quadratic complexity, leading to high inference costs and latency for long sequences. However, attention matrices are mostly sparse,…
cs.CL2025
InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on a Single GPU
Heejun Lee, Geon Park, Jaduk Suh +1
In modern large language models (LLMs), handling very long context lengths presents significant challenges as it causes slower inference speeds and increased memory costs. Addition…