Asynchronous RL buys throughput with fast kernels, a queue and larger batches, and pays for it in the gap between sampler and trainer. Model the gap as an engine-mismatch floor plus staleness, put everything on a data clock, and two settings keep it fixed as the batch grows: the step size follows the optimizer, and the queue is sized in rollouts.
RL training for LLMs is notoriously unstable. While recent studies attribute this to training-inference mismatch from hybrid engines, we show this is not merely a static numerical issue, but a dynamic problem coupled with the model's optimization trajectory. We propose a specialized Learning Rate Scheduler that decays LR as gradient noise rises, using response length surge as a reliable early indicator of impending instability.
RL training for LLMs frequently suffers from training collapse due to exploding gradient variance in long-horizon tasks. We derive the Optimal Token Baseline (OTB) from first principles, proving that updates should be weighted inversely to their accumulated uncertainty (Realized Energy). Our computationally free Logit-Gradient Proxy eliminates training collapse, matches N=32 performance with just N=4, and reduces token consumption by 62-66%.
A rigorous mathematical analysis showing that Top-K expert routing in Mixture of Experts creates two fundamental pathologies: gradient blackout (zero gradients almost everywhere) and first-order approximation failure (discontinuous policy mapping), explaining why MoE-RL training can be unstable.