18 papers · 1 filter
Speculative Forcing for Efficient Autoregressive Video Generation
Hyunwook Choi, Seungryong Kim, Dongyoon Han +1
Few-step autoregressive video generators enable streaming generation, yet they run every token through the final denoising step, regardless of how much refinement it needs. We find…
Learning Transferable World-Action Models from Task-Paired Human Videos
Jeongeun Park, Taekyung Kim, Dongyoon Han +1
World-action models inherit broad visual and physical priors from video pre-training. However, standard within-video next-frame prediction allows them to rely on appearance shared…
On-Policy Distillation with Negative-Policy Rollouts
Jaehui Hwang, Dongyoon Han, Sangdoo Yun +1
On-policy distillation (OPD) has been widely studied as a post-training method in which a student model obtains token-level supervision from a stronger teacher on its own rollouts.…
DLoop: Looped Speculative Decoding
Geonmo Gu, Byeongho Heo, HeeJae Jun +4
Speculative decoding accelerates autoregressive generation in large language models. In each drafting stage, a lightweight draft model proposes tokens that the target model subsequ…
Isotropic Embedding Perturbations for Robust Vision Language Encoders
Hyesong Choi, Daeun Kim, Song Park +5
Data augmentation is fundamental to training modern deep vision and multimodal models. While individual methods, such as RandAug, CutMix, Mixup, RandErase, and DropPath, offer stro…
Router Prior Bias: Preserving Base Routing Structure in MoE Post-Training
Jaedeok Lee, Keonwoo Kim, Dongyoon Han +3
Mixture-of-Experts (MoE) pretraining relies on an auxiliary load-balancing loss (LBL) to drive per-expert utilization toward uniformity. Post-training inherits a different situatio…
Verification-Aware Training for Speculative Decoding
Geonmo Gu, Byeongho Heo, HeeJae Jun +4
Speculative decoding accelerates large language model inference by using a draft model to generate candidate tokens, which are verified by the target model in a single forward pass…
On-Policy Delta Distillation for Multilingual Math Reasoning
Byeongho Heo, Jaehui Hwang, Sangdoo Yun +1
On-Policy Distillation (OPD) is emerging as a promising alternative to reinforcement learning for LLM post-training, yet its effectiveness in multilingual settings remains underexp…
Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs
Seunghyun Lee, Dongyoon Han, Sangdoo Yun
In a dual-use field, hazardous knowledge and the benign expertise around it share vocabulary, concepts, and causal structure. That overlap makes selective refusal hard, and existin…
On-Policy Delta Distillation
Byeongho Heo, Jaehui Hwang, Sangdoo Yun +1
On-policy distillation is an alternative post-training method in reinforcement learning that alleviates the constraints imposed by reward models by providing token-level supervisio…
Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time
Jeongeun Park, Juhan Park, Taekyung Kim +3
Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in bot…
WorldKV: Efficient World Memory with World Retrieval and Compression
Jung Yi, Minjae Kim, Paul Hyunbin Cho +3
Autoregressive video diffusion models have enabled real-time, action-conditioned world generation. However, sustaining a persistent world, where revisiting a previously seen viewpo…
Gaze Attention: Query-Adaptive Visual Routing for Efficient Multimodal LLMs
Junha Song, Byeongho Heo, Geonmo Gu +3
When humans describe a visual scene, they do not process the entire image uniformly; instead, they selectively fixate on regions relevant to their intended description. In contrast…
MEME: Multi-entity & Evolving Memory Evaluation
Seokwon Jung, Alexander Rubinstein, Arnas Uselis +2
LLM-based agents increasingly operate in persistent environments where they must store, update, and reason over information across many sessions. While prior benchmarks evaluate on…
MuCo: Multi-turn Contrastive Learning for Multimodal Embedding Model
Geonmo Gu, Byeongho Heo, Jaemyung Yu +7
Universal Multimodal embedding models built on Multimodal Large Language Models (MLLMs) have traditionally employed contrastive learning, which aligns representations of query-targ…
Grounding World Simulation Models in a Real-World Metropolis
Junyoung Seo, Hyunwook Choi, Minkyung Kwon +10
What if a world simulation model could render not an imagined environment but a city that actually exists? Prior generative world models synthesize visually plausible yet artificia…
Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction
Jang-Hyun Kim, Dongyoon Han, Sangdoo Yun
Efficient key-value (KV) cache management is crucial for the practical deployment of large language models (LLMs), yet existing compression techniques often incur a trade-off betwe…
Oops, Wait: Discourse Tokens Matter in Reasoning Model
Jaehui Hwang, Byeongho Heo, Sangdoo Yun +1
Recent studies suggest that even data-efficient training with (1K) reasoning trajectories can induce non-trivial reasoning capabilities in large language models through pos…