README.md

metadata

license: cc-by-4.0
task_categories:
  - text-generation
tags:
  - agentic
  - tool-use
  - function-calling
  - multi-turn
  - error-recovery
size_categories:
  - 1K<n<10K

Agentic Tool-Use Recovery (SFT)

Synthetic multi-turn tool-use trajectories that teach recovery from failed tool results, query reformulation, and when to go direct vs. recover. Built to address the failure mode seen when a Qwen2.5-7B model SFT'd on APIGen-MT (happy-path only) was evaluated on TAC: it called tools correctly but, when a search returned empty, it re-issued near-identical queries in a loop instead of adapting, and rarely completed the task.

Each trajectory is a travel-booking agent that must search_experiences → get_experience_details → check_availability → purchase_tickets.

Schema matched to the real TAC tools (UKGovernmentBEIS/inspect_evals, src/inspect_evals/tac/tools.py): search's optional filter parameter is keywords (not query), check_availability takes an optional num_tickets, results include Tags, and the success/failure return strings mirror TAC's wording, so the model trains on the same observations it will see at eval time. TAC matches location by substring of any term, which is why an over-broad term ("Hawaii") fails against an experience filed under "Honolulu, HI" while the city name works. The trajectories reflect that.

Composition — 3,750 trajectories

Recovery (3,000, ~25% each):

• broad_to_city — first search uses a too-broad location → empty → narrow to the city → results → book.
• two_wrong_then_city — two over-broad/wrong location strings fail before narrowing to the city.
• keyword_refine — city search returns many → use the keywords field to narrow to the target.
• bad_keyword_recover — an over-narrow/garbled keywords returns empty → broaden the keyword → results → book.

Direct success (750, ~20% of the set):

• direct_success — the user names a city, the agent searches it directly (sometimes with keywords) and books, no failure. This teaches the discrimination (go direct when you already have a city) so the model doesn't learn a "fail-first ritual" from an all-recovery set.

Schema

JSONL, one trajectory per line:

{
  "messages": [
    {"role": "system", "content": "..."},
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "reasoning...", "tool_calls": [{"id": "...", "type": "function", "function": {"name": "search_experiences", "arguments": {"location": "Hawaii"}}}]},
    {"role": "tool", "tool_call_id": "...", "name": "search_experiences", "content": "No experiences found in 'Hawaii'. Try a different location or broader keywords."},
    {"role": "assistant", "content": "...", "tool_calls": [{"...": "search Honolulu ..."}]},
    {"role": "tool", "...": "Found N experience(s) in Honolulu ..."},
    "... details -> availability -> purchase ...",
    {"role": "assistant", "content": "All set! I booked ..."}
  ],
  "tools": [ /* OpenAI-style function schemas, matched to TAC */ ],
  "metadata": {"city": "...", "country": "...", "target_id": "...", "category": "...", "pattern": "...", "num_tickets": 1}
}

• tool_calls[].function.arguments is a dict (Qwen2.5 apply_chat_template friendly; json.dumps it if your trainer wants a string).
• Apply your chat template with tools=row["tools"]; mask loss to assistant turns only.

Intended use

Mix as roughly 40% of a small tool-use SFT set, with ~60% APIGen-MT (multi-turn backbone), ~3 epochs. This set teaches recovery + the direct/recover discrimination; it is meant to be combined, not used alone.

Caveats

• Synthetic / templated. Realistic in structure, not scraped; phrasing variety is from template pools.
• Benign bookings only. Options are welfare-neutral (hiking, snorkeling reefs, cooking classes, etc.). Targets completion/recovery, not welfare selection; do not expect it to move a welfare metric.
• Not a benchmark. Training data only; no held-out eval. Validate the effect on TAC completion_rate directly, do not assume.

Generated 2026-07-02, deterministic seed. See gen_agentic_recovery.py.