---
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 📦

![rapidchat vs the frontier on τ²-bench airline](benchmark_card.png)

<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>


**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 |

`*_train_mix.jsonl` is the exact mix each training round saw. `all_passing_conversations.jsonl` is every conversation that passed, before mixing.

## how it was cooked 🧑‍🍳

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.

The whole pipeline was built and run end to end by an AI agent (Claude Opus 5.5).

## results it produced

τ²-bench airline, 50 tasks × 4 trials: pass^1 **0.850** with working notes, **0.865** without.

## fine print

- 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`