The monolith->package split landed while #558 was adding provider-aware web
search to toolorchestra.py upstream. Rebasing resolved that as delete-vs-modify,
which would have silently dropped the feature: the package always routed search
to Anthropic. Port it -- search now follows cloud_endpoint (openai-web-search /
gemini-web-search / anthropic-web-search), with the matching worker types,
per-search cost surcharges, and pool validation.
_TOOLORCH_SEARCH_TYPES replaces the scattered == "anthropic-web-search" checks,
so the one-shot guard and the "needs a solver" rule now cover every search type
rather than just the Anthropic one -- previously a search worker could have been
picked as the synthesis fallback.
Also, to get back under the ruff gate CI now enforces (it landed in the 95
commits we were behind): formatting, import order, and one real bug -- calculator
had "abs" twice in the same dict literal, and eval_backend had four imports
stranded below a function body.
The build_orchestrator_sft driver test read OJ_DATA_ROOT from the ambient
environment, so it wrote to the real experiments tree instead of tmp_path for
anyone who had sourced .env. Drop the var in the test.
The orchestrator SFT pipeline was living entirely in the working tree and
never got committed. One script in that lineage (clean_sft_data.py) has
already been lost with no way to recover it, so track the rest before the
same thing happens again:
run_sft_fsdp.py full-parameter FSDP trainer (launched via accelerate)
sft_tokenize.py conversation -> tokens + assistant-only masking
render_sft_data.py record -> chat-template rendering
make_splits.py train / holdout / overfit splits
chat_orchestrator.py REPL for eyeballing a trained checkpoint's routing
format_eval_sample.py, upload_to_braintrust.py
scripts/train/ fsdp4.yaml, fsdp8.yaml, eval sbatch
toolorchestra/tracing.py was also untracked despite being imported by
unified.py and rollout.py, which left the branch broken for a fresh
checkout. Add it along with the rest of the ToolOrchestra package split.
Also here: mmlu_pro / supergpqa scorers, expert pricing for the OpenRouter
Qwen builds (estimates, flagged in-file), an expanded calculator/web_search
tool surface, and the reject-sampling clean gate that run_sft_fsdp reads
via --require-clean.
GRPO is left at its upstream state -- the RL stage hasn't been run yet, so
it stays out of this change. Paths in the sbatch are env-driven rather than
hardcoded to one cluster home.
Tools:
- Bridge 4 more real OpenJarvis tools into the orchestrator catalog
(think, apply_patch, pdf_extract, db_query); catalog is now 6 models
+ 11 basic tools.
- Fix the dispatch path so confirmation-gated tools actually run: the
ToolExecutor was built with no confirm callback, so shell_exec (and
any requires_confirmation tool) returned a "requires confirmation"
error instead of executing -- which silently broke TerminalBench.
Headless eval/rollouts now auto-approve.
- Drop dead-end candidates: git_* (need the unbuilt openjarvis_rust
extension and just duplicate shell_exec), browser_* (needs Playwright),
and knowledge_search/sql/retrieval (personal-data RAG, empty on the
academic benchmarks).
SFT data pipeline:
- Reasoning-task loaders (GeneralThought-430K + OpenThoughts3) with an
8K cold-start set and a 30K GRPO prompt pool.
- Domain-dispatched verifier (math/code checkers + Gemini judge fallback,
since the OpenAI key is dead).
- Rejection-sampling generator + unified <tool_call> serializer matching
the GRPO rollout format; base self-sampling driver.
- Drop the old paradigm/tier scaffolding (adp_loader, build, paradigms,
select, serialize, tiers) replaced by the unified path.
Training/eval:
- SFT + GRPO configs for Qwen3.5-9B and gemma-4-12B-it.
- OrchestratorBackend + eval harness over GAIA/TerminalBench/TauBench/
MMLU-Pro/SuperGPQA.
- Cost-aware GRPO reward.
Ignore generated data/ artifacts.
Train a single ~9B orchestrator (Qwen3-8B) for local<->cloud routing via SFT
on synthetic traces derived from NeuLab ADP (agent-data-collection).
- sft_data/: full ADP trajectory loader, tier model + pricing, 4 paradigm
renderers, reward-ranked selection, conversations-JSONL serializer, CLI
- wire sft_trainer._generate_traces() to the pipeline (no GPU / no API keys)
- Qwen3-8B SFT config + scripts/orchestrator CLI wrapper
- offline fixture tests (12)
Heuristic competence labels for the cold-start; real paradigm execution +
GRPO are the documented v2.
Andrew Park