Co-operative time-slicing boosts GPU utilization

A new co-operative time-slicing solution from the llm-d project interleaves independent reinforcement learning jobs onto shared hardware. Benchmarks show aggregate accelerator duty cycles rising from roughly 40% to 70% without affecting model convergence. The approach eliminates idle GPU time during RL post-training phases like sampling and gradient updates.