5 citations · 5 across the 11 of their papers we have counts for
11 papers
Recursive Language Models Generalize Out of Domain
Chenxiao Yang, Zhiyuan Li, David McAllester +1
We study when limiting what a language model can see improves learning. We compare standard CoT, the more general learner that reads the full trace, with recursive language models,…
Tight Sample Complexity of Transformers
Chenxiao Yang, Nathan Srebro, Zhiyuan Li
We tightly characterize the VC dimension of depth- Transformers with a total of parameters, mapping an input sequence of length to a single output, establishing an upper…
Hierarchical Domain Generalization
Chenxiao Yang, Zhiyuan Li, Shai Ben-David +1
We study hierarchical domain generalization as a problem of extrapolation from finite observed regions to an entire instance space, replacing i.i.d. sampling with arbitrary domain…
Training Transformers as a Universal Computer
Ruize Xu, Chenxiao Yang, Yanhong Li +1
We demonstrate that a small transformer can learn to execute programs in MicroPy, a simplified yet computationally universal programming language. Given procedure definitions toget…
Recursive Models for Long-Horizon Reasoning
Chenxiao Yang, Nathan Srebro, Zhiyuan Li
Modern language models reason within bounded context, an inherent constraint that poses a fundamental barrier to long-horizon reasoning. We identify recursion as a core principle f…
On Powerful Ways to Generate: Autoregression, Diffusion, and Beyond
Chenxiao Yang, Cai Zhou, David Wipf +1
Diffusion language models have recently emerged as a competitive alternative to autoregressive language models. Beyond next-token generation, they are more efficient and flexible b…
Let it Calm: Exploratory Annealed Decoding for Verifiable Reinforcement Learning
Chenghao Yang, Lin Gui, Chenxiao Yang +3
Reinforcement learning with verifiable rewards (RLVR) is a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs), yet its success hinges on eff…
Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner
Cai Zhou, Chenxiao Yang, Yi Hu +7
Diffusion language models, especially masked discrete diffusion models, have achieved great success recently. While there are some theoretical and primary empirical results showing…
PENCIL: Long Thoughts with Short Memory
Chenxiao Yang, Nathan Srebro, David McAllester +1
While state-of-the-art LLMs have demonstrated great promise of using long Chains-of-Thought (CoT) to boost reasoning, scaling it up to more challenging problems at test-time is fun…
GeoMix: Towards Geometry-Aware Data Augmentation
Wentao Zhao, Qitian Wu, Chenxiao Yang +1
Mixup has shown considerable success in mitigating the challenges posed by limited labeled data in image classification. By synthesizing samples through the interpolation of featur…
Learning Divergence Fields for Shift-Robust Graph Representations
Qitian Wu, Fan Nie, Chenxiao Yang +1
Real-world data generation often involves certain geometries (e.g., graphs) that induce instance-level interdependence. This characteristic makes the generalization of learning mod…