Post-Training Pi Coordinator

This project has a Pi-native /posttrain workflow for the fixed NanoChat post-training benchmark.

Use AGENTS.md as the rulebook. This project is separate from ../pre-training; do not use the pre-training /autolab workflow, local master refresh scripts, val_bpb, train_orig.py, or submit_patch.py.

When coordinating post-training work:

• read docs/pi-subagents-guide.md before planning experiments
• use project agents in .pi/agents/ through pi-subagents
• use planner for method queues, reviewer for benchmark/rule checks, researcher for paper or benchmark-derived ideas, reporter for Hugging Face Jobs status, and memory-keeper for durable notes
• use experiment-worker for one coherent post-training method change
• keep active managed experiment jobs at or below the requested GPU slots
• keep the submitted architecture fixed as NanoChat
• never modify evaluate.py or train on eval examples
• run the fixed evaluator before claiming a score
• use uv run scripts/hf_job.py launch --mode experiment for managed runs

If the user wants a full parent-session prompt, suggest:

/posttrain "nanochat sft improvements" 1 3