8 papers
Selecting Long-Horizon Trajectories for Reliable and Efficient Terminal-Agent Training
Cuong Dang, Hoang Anh Just, Ruoxi Jia
Terminal agents are commonly trained by imitating long teacher trajectories, yet how much of each trajectory to supervise remains unexplored. We study the \emph{supervision horizon…
Capacity-Dependent Effects of Data Selection for Reasoning
Cuong Dang, Hoang Anh Just, Ruoxi Jia
In reasoning supervised fine-tuning, candidate responses for the same instruction can differ substantially in how well they match the student's current distribution. Recent likelih…
From Weak Cues to Real Identities: Evaluating Inference-Driven De-Anonymization in LLM Agents
Myeongseob Ko, Jihyun Jeong, Sumiran Singh Thakur +2
Anonymization is often assumed to protect privacy once explicit identifiers are removed, because re-identification has historically required specialized expertise, tailored algorit…
More Than the Final Answer: Improving Visual Extraction and Logical Consistency in Vision-Language Models
Hoang Anh Just, Yifei Fan, Handong Zhao +6
Reinforcement learning from verifiable rewards (RLVR) has recently been extended from text-only LLMs to vision-language models (VLMs) to elicit long-chain multimodal reasoning. How…
Retracing the Past: LLMs Emit Training Data When They Get Lost
Myeongseob Ko, Nikhil Reddy Billa, Adam Nguyen +3
The memorization of training data in large language models (LLMs) poses significant privacy and copyright concerns. Existing data extraction methods, particularly heuristic-based d…
Probing Knowledge Holes in Unlearned LLMs
Myeongseob Ko, Hoang Anh Just, Charles Fleming +2
Machine unlearning has emerged as a prevalent technical solution for selectively removing unwanted knowledge absorbed during pre-training, without requiring full retraining. While…
The Signal is in the Steps: Local Scoring for Reasoning Data Selection
Hoang Anh Just, Myeongseob Ko, Ruoxi Jia
Distilling long-form reasoning from teacher models into smaller students requires selecting which candidate solutions to train on. Recent work argues that one should select respons…
Injecting Measurement Information Yields a Fast and Noise-Robust Diffusion-Based Inverse Problem Solver
Jonathan Patsenker, Henry Li, Myeongseob Ko +2
Diffusion models have been firmly established as principled zero-shot solvers for linear and nonlinear inverse problems, owing to their powerful image prior and iterative sampling…