1.1k citations · 3k across the 8 of their papers we have counts for
4 papers · 1 filter
A Cheap Verifier is Good Enough: LLM Post-training is Robust to Erroneous Rewards
Andreas Plesner, Curtis Northcutt, Francisco Guzmán +1
When post-training large language models on tasks with semi-verifiable rewards, there are many factors (training steps, base model size, training order, data quality, verifier accu…
An Imperfect Verifier is Good Enough: Learning with Noisy Rewards
Andreas Plesner, Francisco Guzmán, Anish Athalye
Reinforcement Learning with Verifiable Rewards (RLVR) is widely used for post-training Large Language Models, but practical verifiers can make errors. We study how the rate and str…
On Evaluating Adversarial Robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot +6
Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that wi…
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Anish Athalye, Nicholas Carlini, David Wagner
We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that…