11 papers
AsyncOPD: How Stale Can On-Policy Distillation Be?
Wonjun Kang, Kevin Galim, Seunghyuk Oh +9
On-policy distillation (OPD) trains a student on its own rollouts guided by teacher feedback and is becoming increasingly important for large language model (LLM) post-training. Li…
EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts
Minseo Kim, Minjae Lee, Seunghyuk Oh +7
Reinforcement learning (RL) has become a representative post-training paradigm for large language models (LLMs), enabling strong reasoning and agentic capabilities. However, rollou…
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…
ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMs
Wonjun Kang, Kevin Galim, Seunghyuk Oh +8
While most autoregressive LLMs are constrained to one-by-one decoding, diffusion LLMs (dLLMs) have attracted growing interest for their potential to dramatically accelerate inferen…
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…
Parameter-Efficient Fine-Tuning of State Space Models
Kevin Galim, Wonjun Kang, Yuchen Zeng +2
Deep State Space Models (SSMs), such as Mamba (Gu & Dao, 2024), have become powerful tools for language modeling, offering high performance and linear scalability with sequence len…
Eta Inversion: Designing an Optimal Eta Function for Diffusion-based Real Image Editing
Wonjun Kang, Kevin Galim, Hyung Il Koo
Diffusion models have achieved remarkable success in the domain of text-guided image generation and, more recently, in text-guided image editing. A commonly adopted strategy for ed…
Counting Guidance for High Fidelity Text-to-Image Synthesis
Wonjun Kang, Kevin Galim, Hyung Il Koo +1
Recently, there have been significant improvements in the quality and performance of text-to-image generation, largely due to the impressive results attained by diffusion models. H…
Focus on defocus: bridging the synthetic to real domain gap for depth estimation
Maxim Maximov, Kevin Galim, Laura Leal-Taixé
Data-driven depth estimation methods struggle with the generalization outside their training scenes due to the immense variability of the real-world scenes. This problem can be par…