Skip to content
Advanced Academy Reader
Exit Reader

Efficient RL Parameter Updates for Large Models

RL for massive LLMs faces update delays; optimizations like checkpoint engines reduce this to seconds.

advanced•3 / 7

Implementation Steps

  1. Setup Distributed RL:
    import torch.distributed as dist
dist.init_process_group(backend='nccl')
  2. Checkpoint Integration:
    from torch.utils.checkpoint import checkpoint
def rl_update(params, gradients):
    checkpointed = checkpoint(lambda: compute_loss(params), use_reentrant=False)
  3. Fast Sync:
    • Use sharded checkpoints; broadcast deltas.
  4. Optimization Loop:
    • Async gradient all-reduce; apply in <20s.
Section 3 of 7
Next →