Chris Cundy is a Research Scientist at FAR.AI. He is interested in how to detect and avoid misaligned behavior induced during training.
He received his PhD in Computer Science at Stanford University, advised by Stefano Ermon. During his PhD he published on topics including Causal Inference, Reinforcement Learning, and Large Language Models.
He has previously worked at CHAI, FHI, and was a winner of the OpenAI preparedness challenge.
Involvement
Why does training on insecure code make models broadly misaligned?
Prior work found that training language models to write insecure code causes broad misalignment across unrelated tasks. We hypothesize that constrained optimization methods like LoRA force models to become generally misaligned in order to produce insecure code, rather than misalignment being a side effect. Testing across LoRA ranks 2-512, we found peak misalignment at intermediate ranks (~50), suggesting parameter constraints drive personality modification rather than skill acquisition and may pose unique safety risks.
The Safety Gap Toolkit: Evaluating Hidden Dangers of Open-Source Models
We release an open-source toolkit to measure this gap across different model families and scales, finding that larger models show increasingly dangerous capabilities when the "safety gap" - the difference in dangerous capabilities between open-weight language models with intact safety measures versus those with safeguards removed by bad actors.
The Obfuscation Atlas: Mapping Where Honesty Emerges in RLVR with Deception Probes
Training against white-box deception detectors in a realistic coding environment reveals two obfuscation strategies models can develop: modifying internal representations to evade the detector, or producing deceptive text with justifications that bypass it. However, sufficiently strong KL regularization combined with a detector penalty can suppress both, validating deception detectors as viable training signals against reward hacking.