RL Training Collapses at Step 8: Dev Implements LoRA to Fix GPU Sync
mervenoyann · x · 2026-08-07
A developer shared hands-on experience running reinforcement learning training on a large dense model, noting that runs frequently collapse at step 8 (high entropy, low reward).
To alleviate the massive memory consumption from weights, KV cache, and NCCL weight sync buffers, they implemented LoRA for the trainer, which saves hours on weight synchronization. The author plans to document the findings once the model is released.
Related event: Developers Tackle High-Entropy Crashes in LLM RL Training(2 posts)→
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