Michael Muehlebach

We introduce a quantization-free way to train autoregressive transformers for continuous action decision making, improving on discretized action methods.

We propose OLLA, a projection-free overdamped Langevin framework that enforces both equality and inequality constraints via a deterministic “landing” correction along the manifold normal.