v0.8.0 - served-model task-eval, KD guards, CLI wave complete

Everything since v0.4.0 (details in the CHANGELOG 0.8.0 section). Highlights:

Training science (documented, including failures)

• KD loss vectorized ~16x (28 min first step -> ~1.7 min/step)
• KD negative result measured and documented: a cross-tokenizer teacher drove the student to 20.07 ppl (worse than the 12.61 baseline); bimodal loss curve
• Tokenizer-family guard warns before KD training; both KD volume locks measured (tokenizer alignment AND the verified-flag gate)
• GRPO backend + verifiable-reward registry; ADR-0019 OPST

New CLI surface (replaces the ad-hoc host scripts)

• ops status / ops log, dataset filter --sidecar, dataset split --sidecar
• pipeline kd-pass (resume, per-case timeout, --workers parallel), config set / set-model
• train evaluate --base, train runs, providers check-logprobs
• train task-eval --endpoint: score served models (llama.cpp) without loading weights, with thinking control (Qwen3 chat_template_kwargs)

Ecosystem

• GGUF export to llama-server on the training host; multi-event CTF corpus; ground-truth arbitration; pipeline state tracing.