6 papers
TABED: Test-Time Adaptive Ensemble Drafting for Robust Speculative Decoding in LVLMs
Minjae Lee, Wonjun Kang, Byeongkeun Ahn +6
Speculative decoding (SD) has proven effective for accelerating LLM inference by quickly generating draft tokens and verifying them in parallel. However, SD remains largely unexplo…
ReJump: A Tree-Jump Representation for Analyzing and Improving LLM Reasoning
Yuchen Zeng, Shuibai Zhang, Wonjun Kang +9
Large Reasoning Models (LRMs) are Large Language Models (LLMs) explicitly trained to generate long-form Chain-of-Thoughts (CoTs), achieving impressive success on challenging tasks…
UNCAGE: Contrastive Attention Guidance for Masked Generative Transformers in Text-to-Image Generation
Wonjun Kang, Byeongkeun Ahn, Minjae Lee +4
Text-to-image (T2I) generation has been actively studied using Diffusion Models and Autoregressive Models. Recently, Masked Generative Transformers have gained attention as an alte…
Draft-based Approximate Inference for LLMs
Kevin Galim, Ethan Ewer, Wonjun Kang +3
Optimizing inference for long-context large language models (LLMs) is increasingly important due to the quadratic compute and linear memory cost of Transformers. Existing approxima…
State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for State Space Models
Wonjun Kang, Kevin Galim, Yuchen Zeng +3
State Space Models (SSMs) have emerged as efficient alternatives to Transformers, mitigating their quadratic computational cost. However, the application of Parameter-Efficient Fin…
VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data
Thomas Zeng, Shuibai Zhang, Shutong Wu +13
Process Reward Models (PRMs) have proven effective at enhancing mathematical reasoning for Large Language Models (LLMs) by leveraging increased inference-time computation. However,…