DEER

Training chaotic systems in parallel: a faster path to neural network convergence

Training nonlinear RNNs on chaotic time series usually means choosing between slow sequential computation or unstable parallel methods. Our NeurIPS 2026 spotlight shows you don't have to compromise. By combining DEER's Newton-type iterations with generalized teacher forcing, we stabilized parallel-in-time training on sequences longer than one million time steps, achieving over 100x speedup. This outperforms Mamba and other state space models for dynamical system reconstruction. For deeper coverage of related efficiency advances, see our article on tiered optimizers cutting MoE training memory demands.