| --- |
| license: mit |
| pretty_name: rapidchat-data |
| language: [en] |
| task_categories: [text-generation] |
| tags: [tau2-bench, agents, tool-use, customer-support, synthetic, sft] |
| configs: |
| - config_name: sft_all_passing |
| data_files: sft/all_passing_conversations.jsonl |
| --- |
| # rapidchat-data 📦 |
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|  |
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| <sub>Frontier scores: official τ²-bench leaderboard (user simulator gpt-5.2, standard harness). rapidchat: our runs, user simulator gpt-6.1-sol, custom harness, train split seen in training. Different setups, not a like-for-like ranking.</sub> |
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| **everything that went into [rapidchat](https://huggingface.co/badr7/rapidchat): the practice world, the scenarios, the conversations it learned from, and the receipts.** |
|
|
| ## what's in the box |
|
|
| | path | what it is | |
| |---|---| |
| | `scenarios/db.json` | a re-skinned airline world: 500 users, 2000 reservations, 300 flights. Same schema as the τ²-bench airline database, every person, booking and flight renamed. | |
| | `scenarios/tasks.json` | 1717 practice scenarios in τ²-bench task format, each with a simulated-customer script and the gold tool calls | |
| | `scenarios/split_tasks.json` | the chunks the scenarios were run in | |
| | `sft/` | chat-format training examples (system prompt, tool schemas, full conversation incl. tool calls and kept reasoning) | |
| | `eval/` | full τ²-bench airline trajectories for both reported runs (50 tasks × 4 trials each) | |
| | `code/` | the scenario generators, validators, SFT builder, LoRA trainer and merge script | |
| | `working_notes.txt` | the airline-policy reminders used in the "with notes" setup | |
|
|
| ### scenario families |
|
|
| | family | scenarios | |
| |---|---| |
| | cancel_ok | 213 | |
| | cancel_denied | 177 | |
| | passenger_update | 131 | |
| | two_requests | 116 | |
| | cancel_flown | 101 | |
| | sweep_cancel | 97 | |
| | compensation_false_claim | 84 | |
| | sweep_cabin | 78 | |
| | baggage_add | 74 | |
| | booking | 64 | |
| | compensation_ok | 59 | |
| | be_willing | 55 | |
| | cabin_change | 52 | |
| | multi_change | 52 | |
| | rebook_pay | 45 | |
| | rank_pick | 45 | |
| | flight_change | 39 | |
| | be_nooffer | 35 | |
| | cabin_partial | 26 | |
| | upgrade_then_change | 26 | |
| | passenger_count_denied | 25 | |
| | refuse_then_book | 18 | |
| | flight_change_dest_first | 16 | |
| | two_denied | 15 | |
| | booking_by_rule | 14 | |
| | upgrade_and_bags | 14 | |
| | booking_round_trip | 13 | |
| | baggage_remove_denied | 8 | |
| | booking_one_stop | 7 | |
| | flight_change_denied | 6 | |
| | compensation_denied | 6 | |
| | insurance_add_denied | 6 | |
|
|
| ### training files |
|
|
| | file | examples | |
| |---|---| |
| | `sft/all_passing_conversations.jsonl` | 2427 | |
| | `sft/round1_train_mix.jsonl` | 701 | |
| | `sft/round1b_train_mix.jsonl` | 1167 | |
| | `sft/round2_train_mix.jsonl` | 1278 | |
| | `sft/round3_train_mix.jsonl` | 1172 | |
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| `*_train_mix.jsonl` is the exact mix each training round saw. `all_passing_conversations.jsonl` is every conversation that passed, before mixing. |
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|
| ## how it was cooked 🧑🍳 |
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| 1. **re-skin:** start from the τ²-bench airline database, rename everyone and everything, and drop every user an official task touches. |
| 2. **generate:** scripted scenario families, each with gold actions computed from the database. Every scenario is replayed against the real airline tools by a validator before it is used. |
| 3. **self-play:** the model plays the agent, gpt-6.1-sol plays the customer. Only conversations that score reward 1.0 are kept. |
| 4. **train:** LoRA rank 32, three rounds, merged into the weights. |
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| The whole pipeline was built and run end to end by an AI agent (Claude Opus 5.5). |
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| ## results it produced |
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| τ²-bench airline, 50 tasks × 4 trials: pass^1 **0.850** with working notes, **0.865** without. |
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| ## fine print |
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| - Training data: the model's own conversations that scored reward 1.0, on (a) synthetic airline scenarios generated over a re-skinned copy of the tau2-bench airline database (renamed users, reservations and flights; users referenced by any official task excluded) and (b) the 30 official airline train-split tasks. No official test-split task, and no conversation on one, was used as training data. The round-3 scenario families and the working notes were designed after reviewing failure modes on official tasks, including test-split tasks; the scenarios are new look-alike cases, not copies of test tasks. 30 of the 50 airline tasks (the train split) were seen in training. |
| - The customer side of every conversation was generated by gpt-6.1-sol (low reasoning). The agent side is the model's own output. |
| - Everything here is synthetic. No real people, bookings or payment details. |
|
|
| ## code |
|
|
| - `code/README.md` |
| - `code/merge.py` |
| - `code/mutation_test.py` |
| - `code/oracle_vc_bench.py` |
| - `code/train_lora.py` |
| - `code/validate_r2.py` |
| - `code/validate_r3.py` |
| - `code/validate_vc_bench.py` |
| - `code/vc_bench.py` |
| - `code/vc_bench_r2.py` |
| - `code/vc_bench_r3.py` |
| - `code/vc_bench_r3b.py` |
| - `code/vc_install.py` |
| - `code/vc_sft.py` |
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