local-distillation-lab

Local distillation requires a controlled dataset, a local student model, a privileged teacher setup, and metrics to compare weight-update methods on-device safely. You run local distillation by configuring these components to evaluate training strategies without external hosting, comparing baseline and pedagogical arms.

Yes, you can compare rejection-sampled fine-tuning and distillation without hosted RL by running local on-device training. This approach evaluates improvements on a captured workload using privileged context and local reward signals, ensuring safety and privacy by keeping all training local.

A pedagogical arm in local distillation is a training strategy evaluated alongside baseline and distillation methods. It uses a privileged teacher setup to guide a local student model, optimizing weight-update methods on reproducible workloads without external hosting.

Before evaluating on-device training strategies, you need a controlled dataset, a local student model, a privileged teacher setup, and metrics like reward signals or validators. These components enable safe, reproducible local distillation and fine-tuning comparisons.

Yes, local distillation works for evaluating improvements on a captured workload by comparing weight-update methods like baseline, rejection-sampled fine-tuning, and distillation. It uses privileged context and local metrics to optimize training strategies safely on-device.

Local distillation provides safety and privacy by keeping all training and evaluation on-device without external hosting. It allows developers to compare methods like rejection-sampled fine-tuning and pedagogical arms on reproducible workloads, ensuring data privacy while evaluating training improvements.