One post tagged with "rl"

In Reinforcement Learning (RL) post-training for Large Language Models using algorithms like GRPO, optimizing the ratio of generator (sampler) to trainer throughput is the single largest driver of infrastructure Total Cost of Ownership (TCO). Because typical RL post-training loops alternate sequentially between generation and optimization phases, GPU and TPU clusters sit completely idle for 40% to 60% of their lifecycle.

Today, llm-d introduces a new well-lit path for Co-operative Time-Slicing: the Snapshot Agent. Rather than forcing physical hardware to wait on upstream phases or sit idle during blocking operations, this platform-level capability dynamically interleaves independent RL jobs onto shared hardware blocks, driving aggregate accelerator duty cycles from the 40% baseline up to 70%+ efficiency without altering underlying model convergence or accuracy.

Key Takeaways​

• Near Zero Idle Accelerators: Co-operative time-slicing multiplexes concurrent RL jobs onto shared GPU and TPU hardware, reclaiming stranded compute capacity and boosting duty cycles from ~40% up to 70%+.
• No Model Degradation: Because active jobs retain exclusive access to physical accelerators during their compute windows, there is no loss in token generation throughput or training step convergence.
• Low Code Changes: A lightweight two-call client API (acquire and yield) integrates seamlessly into existing RL loops, while the Snapshot Agent automatically handles CUDA context evacuation and restoration to host DRAM under the hood.