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cs.LG2026
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…
cs.LG2025
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…
cs.LG2024
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…