External Products And Research Reviewed
This page records products, startups, papers, and blogs that changed the posttrainllm roadmap. It is a teardown ledger: what we learned, what we stole, what we rejected, and what still needs a local experiment.
Review Standard
Every external review should answer:
- What is the source?
- What claim or technique matters?
- Is it method-level or recipe-level?
- What is the posttrainllm translation?
- Did we adopt, scaffold, try, reject, or park it?
- What is the next smallest experiment?
Reviewed Sources
| Source | What Mattered | posttrainllm Translation | Status | Next Action |
|---|---|---|---|---|
| TrainLoop AI | Case studies emphasize failed-attempt accounting, slice metrics, trace review, candidate selection, batch-first post-training, policy lag, and LoRA geometry | Added technique registry, SQL candidate-selection tooling, slice metrics, trace review, batch plan renderer, LoRA geometry docs/tooling | scaffolded | Run SQL candidate-selection model experiment |
| TrainLoop NollaMD case study | Multiple-choice/candidate-selection framing can unlock sparse tasks where open generation is too hard | Use SQL candidate selection before open SQL generation | scaffolded | Build/train/eval candidate-selection SQL recipe |
| TrainLoop OAPL writeup | Batch rollouts + offline scoring + compact update; policy lag can stabilize learning | Add batch post-training plan and defer OAPL-style run until reward surface is clean | scaffolded | Use after candidate-selection has evidence |
| TrainLoop LoRA writeup | Effective-update geometry and controlled rank/layer analysis matter | Add LoRA geometry diagnostics and plan a controlled rank sweep | scaffolded | Attach lora-geometry.json to next adapter run |
| TrainLoop Mercor coding-agent post | Coding-native harnesses can beat bespoke tool APIs for knowledge-work agents | Keep future coding-agent/product work file/code-native | parked | Revisit when coding-agent product becomes active |
| Baseten post-training positioning | Post-training is custom data + RL/reward shaping + performance + infra, not generic fine-tune UI | Reframed posttrainllm as Mac-local specialist factory | adopted | Keep factory docs centered on data/post-training/eval/perf/package/report |
| Hugging Face model hub / SQL specialists | Small public SQL models create realistic baselines | Compared against cssupport/t5-small-awesome-text-to-sql; scanned prem-1B-SQL, Qwen SQL variants, SQLCoder | tried | Add public execution benchmark before claiming serious SQL competitiveness |
| Defog SQLCoder / Arctic Text2SQL class models | Strong SQL systems report execution accuracy on serious SQL benchmarks, not only exact string match | Treat BIRD/Spider execution as required next public SQL gate | adopted | Build/run Spider or BIRD Mini execution gate |
| Apple on-device Foundation Models | Useful as a free routing floor, not a capability dependency | Documented measured limitations and ruled out adapter dependency | rejected-for-core | Keep our own model/eval gate as differentiation |
| Castform RL fine-tune platform | Composite rewards and trace-driven data loops | Mapped into composite reward, trace-to-data, reasoning-depth classification | partially-adopted | Integrate reward framework with training loops before RLVR |
| Cline / agent context hierarchy research | Structured-output enforcement and context hierarchy patterns | Added deferred tools/context hierarchy learnings | partially-adopted | Revisit only when coding-agent product returns |
Gaps Exposed By Reviews
- The old roadmap had methods, but not enough recipes.
- External teardown was ad hoc; it must become mandatory before major runs.
- Attempt results were scattered across specialist docs, run reports, and public artifacts.
- Public claims need serious benchmark alignment: exact match is not enough for SQL, and self-reported external metrics must be labeled as directional.
- Docs need validators so discipline is enforced, not just written.
Required Review Before A New Target
Before picking a new factory target, do a short teardown:
- Three competitor/startup examples.
- Three relevant papers/blogs.
- Three techniques worth trying.
- Three techniques rejected and why.
- One cheapest local recipe selected for the first run.
Store the result here or in a target-specific file under docs/techniques/.