Automated agent loop: advantage gradients to GGUF weights overnight
cephaloform · x · 2026-08-18
A developer shared an automated local agent training pipeline. After judgment, the value model updates memory with real rewards and task duration. Advantages are calculated as 'realized reward - value estimation', then converted to gradients and weight updates. New weights are automatically converted to GGUF and served before the developer wakes up, closing the loop from interaction to model iteration.
Related event: Dev Builds Local Agent That Trains Itself Overnight(4 posts)→
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