Hugh Zhang

I'm currently a member of technical staff at a stealth AI startup. Previously, I worked as an AI researcher at Scale AI, and before that I was a PhD candidate at Harvard advised by David Parkes and supported by the NSF Graduate Research Fellowships Program and a Kempner Institute Graduate Fellowship. Even before that, I was a software engineer at Asana (gap year before college). In my spare time, I've been a lifelong Go player (in fact, seeing AlphaGo beat Lee Sedol was the origin of my interest in AI). I also co-founded the Gradient, a digital magazine focusing on AI.

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My recent work focuses on evals, test-time compute, and post-training for LLMs. Previously, I worked on multi-agent reinforcement learning and game theory. * denotes equal or alphabetical ordering.

Reconstructed o1 test-time scaling laws using public API access to o1-mini.

A frontier AI benchmark.

Searching over a diverse set of ideas/plans in natural language significantly helps code generation and is far more effective than repeated sampling.

We demonstrate that multi-turn human jailbreaks can achieve >70% success rates against LLM defenses that report single-digit success rates for automated single-turn attacks.

We clone GSM8k to measure dataset contamination. Some models show signs of overfitting, but frontier models show strong generalization.

Representation learning may be useful for post-training LLMs.

A lightweight alternative to fine-tuning that performs better than LORA for very small datasets and requires minimal model access.

Training on chain-of-thoughts that lead to a correct answer can help a LLM self-improve and generalize far beyond their original capabilities in the toy environment of addition.

Unified algorithm for both reinforcement learning and game theory. Can solve MDPs as fast as RL methods and imperfect-information games as fast as CFR using the single set of hyperparameters.

Human level performance in the game of Diplomacy, where agents negotiate with other humans in natural language.

A novel no-regret learning procedure that converges to correlated and coarse-correlated equilibria several orders of magnitude faster than previous methods in randomly generated normal-form games.

The first large-scale evaluation of decoding methods for large language models along the entire quality-diversity spectrum.

Alongside Celli et al. (2020) (concurrent work), this paper gives the first internal regret minimization dynamics for extensive-form games.

Existing language models can generate either high quality or diverse utterances, but not both simultaneously. How can we measure that in a single metric?

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