1 citations · 1 across the 8 of their papers we have counts for
10 papers
-OPSD: Deriving with Policy Optimization, Training with Self-Distillation
Jiawei Xu, Minghui Liu, Juzheng Zhang +2
On-policy self-distillation (OPSD) is a promising approach to improve reasoning language models, but it remains brittle in practice: making it work reliably often requires substant…
Not All LLM Reasoning is Visible in the Chain-of-Thought
Vatsal Baherwani, Tom Goldstein, Ashwinee Panda
A key question for AI safety is whether a language model expresses all of its reasoning in its output tokens. We demonstrate a concrete failure mode where frontier models exhibit i…
Speculating Experts Accelerates Inference for Mixture-of-Experts
Vivan Madan, Prajwal Singhania, Abhinav Bhatele +2
Mixture-of-Experts (MoE) models have gained popularity as a means of scaling the capacity of large language models (LLMs) while maintaining sparse activations and reduced per-token…
GATES: Self-Distillation under Privileged Context with Consensus Gating
Alex Stein, Furong Huang, Tom Goldstein
We study self-distillation in settings where supervision is unreliable: there are no ground truth labels, verifiable rewards, or external graders to evaluate answers. We focus on d…
Image Generation with a Sphere Encoder
Kaiyu Yue, Menglin Jia, Ji Hou +1
We introduce the Sphere Encoder, an efficient generative framework capable of producing images in a single forward pass and competing with many-step diffusion models using fewer th…
FineGRAIN: Evaluating Failure Modes of Text-to-Image Models with Vision Language Model Judges
Kevin David Hayes, Micah Goldblum, Vikash Sehwag +3
Text-to-image (T2I) models are capable of generating visually impressive images, yet they often fail to accurately capture specific attributes in user prompts, such as the correct…
DynaGuard: A Dynamic Guardian Model With User-Defined Policies
Monte Hoover, Vatsal Baherwani, Neel Jain +7
Guardian models play a crucial role in ensuring the safety and ethical behavior of user-facing AI applications by enforcing guardrails and detecting harmful content. While standard…
Analysis of Attention in Video Diffusion Transformers
Yuxin Wen, Jim Wu, Ajay Jain +2
We conduct an in-depth analysis of attention in video diffusion transformers (VDiTs) and report a number of novel findings. We identify three key properties of attention in VDiTs:…
Dense Backpropagation Improves Training for Sparse Mixture-of-Experts
Ashwinee Panda, Vatsal Baherwani, Zain Sarwar +4
Mixture of Experts (MoE) pretraining is more scalable than dense Transformer pretraining, because MoEs learn to route inputs to a sparse set of their feedforward parameters. Howeve…
LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank Adaptation
Juzheng Zhang, Jiacheng You, Ashwinee Panda +1
Low-Rank Adaptation (LoRA) has emerged as a popular parameter-efficient fine-tuning (PEFT) method for Large Language Models (LLMs), yet it still incurs notable overhead and suffers…