S-FLM is a hyperspherical latent flow language model that improves continuous flow language models on large-vocabulary reasoning tasks and closes the gap to masked diffusion at standard sampling temperature.
Weiss, Niru Maheswaranathan, and Surya Ganguli
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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.
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Language Modeling with Hyperspherical Flows
S-FLM is a hyperspherical latent flow language model that improves continuous flow language models on large-vocabulary reasoning tasks and closes the gap to masked diffusion at standard sampling temperature.
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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.