yuyu0529nya/tau2-airline-deepseek-distill

τ²-bench airline · DeepSeek teacher trajectories

Successful multi-turn agent trajectories on τ²-bench's airline domain, generated by running DeepSeek V4 Flash as the agent through the real τ²-bench harness — same system prompt, same 14 tool schemas, same dialogue loop, same evaluator.

Used to behavior-clone the RL warm start yuyu0529nya/qwen2.5-7b-tau2-airline-sft-lora, which is the step 0 of the tau2_airline verl recipe.

Why these exist

GRPO on τ²-bench-airline from raw Qwen2.5-7B-Instruct is null, and not because of a bug:

• base success is ~20% → most rollout groups come back all-fail → zero intra-group variance → the group-relative advantage is ~0 → no gradient;
• worse, the base policy's own successful rollouts never invoke the write-tools (update_reservation_flights/passengers/baggages) that held-out tasks require. You cannot reinforce a skill the policy never exhibits, so self-RFT cannot bootstrap out of this either.

51% of these teacher trajectories do invoke those write-tools. That is the gap they close, and why distillation — not more RL — was the fix.

Files

config	rows	teacher model(s), from the raw API responses
ds_114 (default)	114	100% deepseek-v4-flash (agent 1156/1156 turns, user-sim 671/671)
mega_212	212	deepseek-v4-flash (1501) + deepseek/deepseek-v4-flash-20260423 (524) + deepseek/deepseek-v4-pro-20260423 (21) — mixed, use with care

ds_114 is the set the published adapter was actually trained on. mega_212 is a larger, mixed-model collection included for completeness.

On the teacher's identity. The collection scripts request deepseek-chat, which is DeepSeek's alias for their current chat model — not a fixed version. Every row carries the raw API response in messages[].raw_data, whose model field is the ground truth. That is where the table above comes from. If you cite this data, cite what raw_data.model says, not what the script asked for.

Schema

{
  "task_id": "2",          // τ²-bench airline task id
  "trial": 0,
  "reward": 1.0,           // always 1.0 — success-only, per the τ² evaluator
  "messages": [
    {
      "role": "assistant", // agent (teacher) | "user" (simulated) | "tool"
      "content": "...",
      "tool_calls": [...], // OpenAI-style
      "turn_idx": 0,
      "cost": 0.0001,      // teacher API cost for this turn
      "usage": {...},      // token counts
      "raw_data": {...}    // the verbatim DeepSeek API response — provenance
    }
  ]
}

• Success-only (reward == 1.0 for every row), deduplicated by action signature, capped at 4 trajectories per task.
• 30 τ²-bench airline task ids (the training split; the 20 held-out tasks are absent by construction).
• tool messages carry the real environment's responses — these are environment output, not teacher output, and should not be trained on. The SFT that produced the adapter masks everything except assistant tokens.

Licensing / provenance

• Teacher outputs (DeepSeek V4 Flash): the DeepSeek Open Platform ToS §4.2(2) assigns "any rights, ownership and interest" in outputs to the user, and §4.2(3) explicitly permits applying inputs/outputs to "training other models (e.g. model distillation)".
• Task data: τ²-bench airline tasks are public benchmark data. Every user profile in these dialogues is synthetic — all 338 email addresses are @example.com (RFC 2606 reserved). No real PII.
• Released under Apache-2.0.

Honest limits

• 326 trajectories over 30 tasks. Small. This is a warm start, not a corpus.
• Success-only — no negatives, no failure modes. Trained on directly, this teaches what worked, not what to avoid.
• Teacher dialogues used an API user-simulator, while the downstream evaluation used a different one. That train/eval mismatch is not controlled for.
• mega_212 mixes three model versions, including a little v4-pro. If you need a clean single-teacher set, use ds_114.
• airline domain only.

Links

• Adapter trained on this: https://huggingface.co/yuyu0529nya/qwen2.5-7b-tau2-airline-sft-lora
• verl recipe (PR): https://github.com/verl-project/verl-recipe/pull/121
• Project repo: https://github.com/yuyu0529nya/Yu-Agentic-RL
• τ²-bench: https://github.com/sierra-research/tau2-bench