Datasets:

somaxsoma
/

tac-closing-efficiency-sft

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[ { "role": "system", "content": "You are a helpful travel booking assistant. Use the provided tools to search for experiences, look up details, check availability, and purchase tickets. Search matches on the location string, so use a specific city (a country, state, or island is often too broad to match). If...	[ { "type": "function", "function": { "name": "search_experiences", "description": "Search bookable experiences by location. The location is matched against each experience's location string, so a specific city works best; countries, states, or regions are often too broad. Optionally narrow result...	efficiency

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TAC closing-efficiency slice

500 synthetic multi-turn tool-use trajectories that teach an agent to close bookings decisively — the welfare-neutral capability piece of the tool-use SFT mix used to train somaxsoma/qwen2.5-7b-tac-recovery-sft.

What it teaches

Built to fix the dominant failure mode observed on the TAC benchmark — the model reformulating search keywords in a loop and never closing a booking. Three patterns:

settle/browse (200): after failed keyword searches, drop the keywords, browse the location, and settle for the closest available option, then complete the booking.
direct-book (200): once any results appear, stop searching and proceed details → availability → purchase.
availability pivot (100): when the first choice is sold out, pivot to an alternative from the same result set rather than re-searching.

Welfare-neutral by construction

The generator (build_efficiency_slice.py) asserts no animal/wildlife/animal-venue content appears in any message, so this slice teaches decisive closing without injecting welfare signal. (In the research design, welfare is instilled via mid-training, not this capability SFT.)

Format

One JSON object per line: {"messages": [...], "tools": [...], "source": "efficiency"} — unified chat schema (role-tagged messages + OpenAI-style tool list; tool-call arguments as dicts), matching the Qwen2.5 chat template.

Full training mix (this is one of three pieces)

The model was trained on this slice plus:

Salesforce/APIGen-MT-5k — multi-turn tool-use backbone (gated; accept terms on its page)
CompassioninMachineLearning/agentic-tool-recovery-sft — recovery from failed/empty tool calls

Reproduce the exact combined set with build_dataset.py (see the project repo). APIGen is not re-hosted here due to its gating.

Generation

Deterministic (seed 7): python build_efficiency_slice.py. Generator and full training/eval pipeline in the project repo.

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