Robin Jia

Achieving superior out-of-distribution generalization, TOPL reveals that focusing on token-level correctness can significantly enhance model performance across diverse generation tasks.

Stochastic eviction can boost reasoning model accuracy by protecting critical value states while achieving significant memory savings.

Despite architectural differences, language models exhibit convergent evolution by learning similar periodic features for number representation, but achieving geometric separability depends on subtle training factors.