6 papers
Provable Benefits of RLVR over SFT for Reasoning Models: Learning to Backtrack Efficiently
Stanley Wei, Juno Kim
Recent advances in large language models (LLMs) have demonstrated that reinforcement fine-tuning of pretrained base models can lead to significant gains in reasoning performance at…
Mirror Mean-Field Langevin Dynamics
Anming Gu, Juno Kim
The mean-field Langevin dynamics (MFLD) minimizes an entropy-regularized nonlinear convex functional on the Wasserstein space over , and has gained attention recently…
Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points
Naoya Yamamoto, Juno Kim, Taiji Suzuki
Wasserstein gradient flow (WGF) is a common method to perform optimization over the space of probability measures. While WGF is guaranteed to converge to a first-order stationary p…
Transformers Provably Solve Parity Efficiently with Chain of Thought
Juno Kim, Taiji Suzuki
This work provides the first theoretical analysis of training transformers to solve complex problems by recursively generating intermediate states, analogous to fine-tuning for cha…
Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation
Juno Kim, Denny Wu, Jason Lee +1
A key paradigm to improve the reasoning capabilities of large language models (LLMs) is to allocate more inference-time compute to search against a verifier or reward model. This p…
Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
Juno Kim, Dimitri Meunier, Arthur Gretton +2
We provide a convergence analysis of deep feature instrumental variable (DFIV) regression (Xu et al., 2021), a nonparametric approach to IV regression using data-adaptive features…