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README.md

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+---
+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** and **query reformulation**, the specific gap observed 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 follows a travel-booking agent that must `search_experiences` → `get_experience_details` → `check_availability` → `purchase_tickets`. The training signal is what to do when a search comes back empty.
+
+## Recovery patterns (balanced, ~25% each)
+
+- **broad_to_city** — first search uses a too-broad location (country / state / island) → 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 results → use the `query` keyword field to narrow to the right one.
+- **bad_query_recover** — an over-narrow/garbled `query` returns empty → broaden the keyword → results → book.
+
+## Schema
+
+JSONL, one trajectory per line:
+
+```json
+{
+  "messages": [
+    {"role": "system", "content": "..."},
+    {"role": "user", "content": "..."},
+    {"role": "assistant", "content": "reasoning...", "tool_calls": [{"id": "...", "type": "function", "function": {"name": "search_experiences", "arguments": {"location": "Ireland"}}}]},
+    {"role": "tool", "tool_call_id": "...", "name": "search_experiences", "content": "No experiences found for 'Ireland'. ..."},
+    {"role": "assistant", "content": "...", "tool_calls": [{"...": "search Galway ..."}]},
+    {"role": "tool", "...": "Found N experiences ..."},
+    "... details -> availability -> purchase ...",
+    {"role": "assistant", "content": "All set! I booked ..."}
+  ],
+  "tools": [ /* OpenAI-style function schemas */ ],
+  "metadata": {"city": "...", "country": "...", "target_id": "...", "category": "...", "pattern": "...", "num_tickets": 1}
+}
+```
+
+- `tool_calls[].function.arguments` is a **dict** (Qwen2.5 `apply_chat_template` friendly; if your trainer wants a JSON string, `json.dumps` it).
+- Apply your chat template with `tools=row["tools"]`. Mask the loss to assistant turns only.
+
+## Intended use
+
+Mix as roughly **40%** of a small tool-use SFT set, with ~60% APIGen-MT (the multi-turn backbone), for ~3 epochs. This dataset teaches *recovery*, not the happy path, so it is meant to be combined, not used alone.
+
+## Caveats
+
+- **Synthetic / templated.** Realistic in structure, not scraped. Phrasing variety comes from template pools, not per-sample LLM generation.
+- **Benign bookings only.** The booked options are welfare-neutral (hiking, snorkeling reefs, cooking classes, etc.). This set targets *completion/recovery*, not welfare selection; do not expect it to move a welfare metric.
+- **Not a benchmark.** Training data only; there is no held-out eval here.
+
+Generated 2026-07-02 with a deterministic seed. See `gen_agentic_recovery.py`.