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Qinyi Sun

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5 papers
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5

TMLR Journal 2025 Journal Article

DafnyBench: A Benchmark for Formal Software Verification

  • Chloe R Loughridge
  • Qinyi Sun
  • Seth Ahrenbach
  • Federico Cassano
  • Chuyue Sun
  • Ying Sheng
  • Anish Mudide
  • Md Rakib Hossain Misu

We introduce DafnyBench, the largest benchmark of its kind for training and evaluating machine learning systems for formal software verification. We test the ability of LLMs such as GPT-4 and Claude 3 to auto-generate enough annotations for the Dafny formal verification engine to successfully verify over 750 programs with about 53,000 lines of code. The best model and prompting scheme achieved 68% success rate, and we quantify how this rate improves when retrying with error message feedback and how it deteriorates with the amount of required code and annotations. We hope that DafnyBench will enable rapid improvements from this baseline as LLMs and verification techniques grow in quality.

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RLC Conference 2025 Conference Paper

Focused Skill Discovery: Learning to Control Specific State Variables while Minimizing Side Effects

  • Jonathan Colaço Carr
  • Qinyi Sun
  • Cameron Allen

Skills are essential for unlocking higher levels of problem solving. A common approach to discovering these skills is to learn ones that reliably reach different states, thus empowering the agent to control its environment. However, existing skill discovery algorithms often overlook the natural state variables present in many reinforcement learning problems, meaning that the discovered skills lack control of specific state variables. This can significantly hamper exploration efficiency, make skills more challenging to learn with, and lead to negative side effects in downstream tasks when the goal is under-specified. We introduce a general method that enables these skill discovery algorithms to learn focused skills---skills that target and control specific state variables. Our approach improves state space coverage by a factor of three, unlocks new learning capabilities, and automatically avoids negative side effects in downstream tasks.

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RLJ Journal 2025 Journal Article

Focused Skill Discovery: Learning to Control Specific State Variables while Minimizing Side Effects

  • Jonathan Colaço Carr
  • Qinyi Sun
  • Cameron Allen

Skills are essential for unlocking higher levels of problem solving. A common approach to discovering these skills is to learn ones that reliably reach different states, thus empowering the agent to control its environment. However, existing skill discovery algorithms often overlook the natural state variables present in many reinforcement learning problems, meaning that the discovered skills lack control of specific state variables. This can significantly hamper exploration efficiency, make skills more challenging to learn with, and lead to negative side effects in downstream tasks when the goal is under-specified. We introduce a general method that enables these skill discovery algorithms to learn focused skills---skills that target and control specific state variables. Our approach improves state space coverage by a factor of three, unlocks new learning capabilities, and automatically avoids negative side effects in downstream tasks.

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TMLR Journal 2025 Journal Article

Fractal Generative Models

  • Tianhong Li
  • Qinyi Sun
  • Lijie Fan
  • Kaiming He

Modularization is a cornerstone of computer science, abstracting complex functions into atomic building blocks. In this paper, we introduce a new level of modularization by abstracting generative models themselves into atomic modules. Our method constructs generative models by recursively invoking atomic generative modules, resulting in architectures with fractal-like, self-similar properties. We call this new class of models fractal generative models. As a running example, we instantiate our fractal framework using autoregressive models as the atomic modules and examine it on the challenging task of pixel-by-pixel image generation. Our experiments show strong performance in both likelihood estimation and generation quality. We hope this work could serve as a starting point for future research into fractal generative models, establishing a new paradigm in generative modeling.

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TMLR Journal 2024 Journal Article

Universal Neurons in GPT2 Language Models

  • Wes Gurnee
  • Theo Horsley
  • Zifan Carl Guo
  • Tara Rezaei Kheirkhah
  • Qinyi Sun
  • Will Hathaway
  • Neel Nanda
  • Dimitris Bertsimas

A basic question within the emerging field of mechanistic interpretability is the degree to which neural networks learn the same underlying mechanisms. In other words, are neural mechanisms universal across different models? In this work, we study the universality of individual neurons across GPT2 models trained from different initial random seeds, motivated by the hypothesis that universal neurons are likely to be interpretable. In particular, we compute pairwise correlations of neuron activations over 100 million tokens for every neuron pair across five different seeds and find that 1-5\% of neurons are universal, that is, pairs of neurons which consistently activate on the same inputs. We then study these universal neurons in detail, finding that they usually have clear interpretations and taxonomize them into a small number of neuron families. We conclude by studying patterns in neuron weights to establish several universal functional roles of neurons in simple circuits: deactivating attention heads, changing the entropy of the next token distribution, and predicting the next token to (not) be within a particular set.

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v2026.09.13