1 citations · 1 across the 6 of their papers we have counts for
6 papers
Generalization Hacking: Models Can Game Reinforcement Learning by Preventing Behavioral Generalization
Frank Xiao, Mary Phuong
Model post-training, and in particular reinforcement learning (RL), is one of the primary mechanisms by which developers can shape models' values and behaviors. However, as models…
Bootstrapped Monitoring: Leveraging Transparent Reasoning to Oversee Stronger AI Agents
Frank Xiao, Mary Phuong
Trusted monitoring is a cornerstone of AI control. However, as frontier models grow more capable, the increasing capabilities gap between trusted and untrusted models may render tr…
Probe-Based Data Attribution: Discovering and Mitigating Undesirable Behaviors in LLM Post-Training
Frank Xiao, Santiago Aranguri
We propose probe-based data attribution, a method that traces behavioral changes in post-trained language models to responsible training datapoints. By computing activation-differe…
Why Do Language Model Agents Whistleblow?
Kushal Agrawal, Frank Xiao, Guido Bergman +1
The deployment of Large Language Models (LLMs) as tool-using agents causes their alignment training to manifest in new ways. Recent work finds that language models can use tools in…
SpatialTraceGen: High-Fidelity Traces for Efficient VLM Spatial Reasoning Distillation
Gio Huh, Dhruv Sheth, Rayhan Zirvi +1
While Vision-Language Models (VLMs) excel in many areas, they struggle with complex spatial reasoning, which requires problem decomposition and strategic tool use. Fine-tuning smal…
Image augmentation improves few-shot classification performance in plant disease recognition
Frank Xiao
With the world population projected to near 10 billion by 2050, minimizing crop damage and guaranteeing food security has never been more important. Machine learning has been propo…