S-FLM is a hyperspherical latent flow language model that learns velocity fields on the unit sphere to generate token sequences via deterministic ODE integration without materializing one-hot vectors.
Weiss, Niru Maheswaranathan, and Surya Ganguli
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
unclear 1representative citing papers
ISEP expands action support in offline RL via value interpolation between data and policy samples, then uses stochastic policy optimization to avoid mode collapse in the resulting multimodal objective.
citing papers explorer
-
Language Modeling with Hyperspherical Flows
S-FLM is a hyperspherical latent flow language model that learns velocity fields on the unit sphere to generate token sequences via deterministic ODE integration without materializing one-hot vectors.
-
ISEP: Implicit Support Expansion for Offline Reinforcement Learning via Stochastic Policy Optimization
ISEP expands action support in offline RL via value interpolation between data and policy samples, then uses stochastic policy optimization to avoid mode collapse in the resulting multimodal objective.