Session — from frontier-parity distillation to the self-improving loop

Date: 2026-06-16. The arc, the decisions, and the transferable lessons. Numbers and mechanics live in tool-calling-frontier-parity.md §8.1–8.5; this is the meta-narrative.

The arc in one paragraph

Started with “get the 4B (or 8B) to the multi-turn bar.” Distillation took it to frontier-parity on file-ops (58→100, matching gpt-5.5, beating Gemma-12B at ⅓ size) — no 8B needed. Tried to break it with a harder gate; it saturated even longer-horizon file-ops. The real question turned out to be breadth, not depth — and there the specialist had regressed (negative transfer). Chasing the fix proved a sharp boundary on cheap distillation and converged the whole thread onto self-improving RL as the answer. Ended building the (teacher-free) ReST loop + a batched throughput path.

Transferable lessons (the keepers)

1. The verifier is the moat, not the trainer. Every advance hinged on a sound, frontier-validated gate. Where no verifier exists (we discussed humor / Reddit-voice), you cannot RL or self-improve — you’re stuck with SFT + vibes. Invest in rewards, not just training.
2. Distillation reaches frontier-parity cheaply — on verifiable, prompt-derivable tasks. The 4B matched a frontier model on file-ops agency. The thesis holds for a domain.
3. Gold-cloning ≡ distillation only when call args come from the prompt. For data-dependent agency (args from tool results — 52% of multi-backend turns), cloning concrete gold values teaches hallucination. The skill is the interleaving (call → read → use), which the gold doesn’t contain → you need real interleaved trajectories (a teacher, or the model’s own filtered rollouts). §8.5.
4. Specialization erodes breadth. Narrow distill: file-ops 100 / out-of-domain 60→42; multi-backend gold 60→31. The stock model stayed the best generalist. “How much intelligence did we keep?” is now a first-class metric → capability-retention PRD.
5. Saturation is real; keep raising the gate. “then it’s not hard enough” → veryhard tier → still 100 → breadth became the discriminating axis. A gate that doesn’t separate top models is done.
6. The convergence. Fixing breadth and proving self-improvement are the same experiment: the model’s own checker-passing rollouts carry correct interleaving with no teacher.

Decisions (log)

Gotchas worth remembering

• /tmp gets wiped — persist fused models under ~/.cache/tinygpt/models/.
• --grad-checkpoint is mandatory for long-seq SFT (18-tool catalog floors examples ~3.5k tokens; 151k-vocab logits OOM the backward without it).
• BFCL global-state isolation — instances are keyed by (model_name, test_id, class) in module globals and reused; concurrent/repeated same-id rollouts corrupt shared state. Fix: unique model_name per rollout (rollout_fast.py). K=1 eval was always clean (checker uses a separate _eval namespace).
• Batch=1 rollout is the loop’s bottleneck — a self-improving loop is impractical without batched rollouts (rollout_fast.py) + (next) prefix-caching the static tools prompt.

Artifacts produced

• Docs: journey §8.1–8.5; PRDs self-improving-agents, local-model-arena-selfplay, capability-retention; learning gap-fixes essential-vs-optimization, webgpu-execution-model, + the roadmap exit-exam.
• Scripts: gen_multiturn_trajdata (+veryhard), gold_to_sft_traj, render_sft_from_traj, distill_multiturn, headtohead_multiturn, bfcl_multiturn_codex (free frontier), rest_iterate, rollout_fast; harness rollout-dumping.
• Models: mt4b_fused (file-ops specialist, 100/95), mt4b_mb_fused (multi-backend, 100 depth / 31 breadth).

Open / next

• ReST first-iteration result (running) — does breadth rise above stock’s 60% with no teacher?
• Validate rollout_fast.py vs the single-rollout harness, then run iteration 2 fast.
• If cold-start ReST is flat: auto-curriculum (target the 30–70% band) or a frontier cold-start.