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Model: BillyWang1/qwen3-8b-tau-sft Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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datasets:
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- fuvty/tau-bench-synthetic
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- tau-bench
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- tool-calling
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- agent
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- sft
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- cold-start
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- qwen3
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---
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# qwen3-8b-tau-sft
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Non-thinking cold-start SFT of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) for
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[tau-bench](https://github.com/sierra-research/tau-bench) retail, trained on successful
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synthetic retail trajectories from
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[fuvty/tau-bench-synthetic](https://huggingface.co/datasets/fuvty/tau-bench-synthetic).
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This is the **epoch-1** checkpoint, which is the one that passed the pre-RL gates and was
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used to initialize GRPO. It is a *behavior-priming* checkpoint: it teaches the model to act
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as a non-thinking, one-tool-call-per-turn tau-bench agent. It is not a finished tau-bench
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policy on its own — see [Results](#results).
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## What "non-thinking" means here
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The tokenizer/chat template is a patched Qwen3 template whose assistant branch renders
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`<|im_start|>assistant\n<think>\n\n</think>\n\n` + content verbatim, and whose generation
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prompt ends with that same empty-think prefill. Because `</think>` is therefore *prompt-side*,
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think-relapse is structurally impossible even at temperature 1.0. The patched
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`chat_template.jinja` and the embedded copy in `tokenizer_config.json` ship with this repo,
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so `apply_chat_template` reproduces the training format exactly.
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Tool calls are emitted as inline Hermes text in the assistant content (the patched template
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does **not** render structured `tool_calls` lists):
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```
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{optional user-visible text}
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<tool_call>
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{"name": "<fn>", "arguments": {<args object>}}
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</tool_call>
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```
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At most one call per turn. Tool results are supplied as `role: "tool"` messages, which the
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template wraps in `<tool_response>…</tool_response>` inside a user turn.
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## Training data
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795 reward-1.0 retail trajectories from `traj-GLM5` were filtered down to **595 rows over 98
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tasks**:
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- airline dropped entirely (it is a held-out zero-shot transfer eval)
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- **12 of 180** synthetic retail tasks dropped by a contamination gate that compares
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write-action goal signatures against the shipped tau-bench retail task sets. All 12 were
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retail-**train** collisions; **zero** collided with retail-**test**
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- 135 trajectories dropped for containing a turn with more than one tool call
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- reasoning traces (`reasoning_content`) stripped; the tau2 greeting turn dropped
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Loss falls on exactly `assistant content + <|im_end|>`. The assistant prefill header, the
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inter-turn newline and every observation block are present but masked out, so the training
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token stream is byte-identical to what an inference server is fed.
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## Training
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| | |
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|---|---|
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| base | Qwen/Qwen3-8B |
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| framework | slime 0.3.0 (Megatron), 4×H100 |
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| rows / batch | 595 / 32 |
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| steps | 54 (18 per epoch × 3); **this repo is epoch 1 = 18 steps** |
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| lr | 1e-5 cosine, min 1e-6, warmup 0.1 |
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| parallelism | TP2, seq-parallel, bf16 |
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| loss | token-level SFT on pre-tokenized rows |
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Final loss 0.118 at epoch 3; this epoch-1 checkpoint was selected because it already passed
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the boundary-token gate below and further epochs were not needed for priming.
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## Gates
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**Boundary-token probe** (does SFT actually train the turn-ending specials?):
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| bucket | n | mean p | median | frac < 0.5 |
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|---|---|---|---|---|
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| `<\|im_end\|>` | 64 | **0.9625** | 0.9983 | 1.6% |
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| `<tool_call>` / `</tool_call>` | 62 | 0.9786 | 0.9998 | 0.0% |
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| content | 7108 | 0.8867 | 0.9994 | 10.0% |
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**Format/geometry gate**: converted rows were replayed through the actual multi-turn rollout
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code with the row's turns scripted as sampled outputs, asserting exact-token prefix alignment
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of the generation prompt at every turn, loss-token equivalence, tool-parser round-trip, and
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eos invariants — **595/595 passed**. Longest assistant turn: 613 tokens.
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## Results
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Evaluated with **greedy** decoding on tau-bench retail-test (115 tasks) and airline (50 tasks,
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never trained on), with a `gpt-4.1-mini` user simulator.
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| | retail-test | airline |
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|---|---|---|
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| **this checkpoint (SFT only)** | 0.043 | 0.020 |
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| after 32 GRPO steps from this init | **0.322** | 0.060 |
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| Qwen3-8B *thinking*, zero-shot (reference) | 0.391 | 0.240 |
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Sampled (T=1.0, 4 samples/task) after those 32 GRPO steps, where the decoding pathology
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is absent:
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| | pass@1 | pass@2 | pass@4 |
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|---|---|---|---|
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| retail-test | 0.315 | 0.443 | 0.574 |
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| airline (never trained on) | 0.140 | 0.237 | 0.360 |
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**Read the SFT-only number carefully.** 0.043 greatly understates the policy: under sampling
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(T=1.0) the same weights score **0.637** average success on retail-train with
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**pass@16 ≈ 0.97**. The gap is a greedy-decoding pathology — 93% of greedy eval episodes
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exhaust their 30-turn budget in a repetition loop rather than terminating. Subsequent GRPO
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mostly *repaired that pathology* (greedy truncation 0.93 → 0.33, repetition 0.48 → 0.15)
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rather than adding raw capability, which is why greedy retail-test climbs 7.5× while sampled
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training reward barely moves.
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So: this checkpoint is a strong **sampled** tool-use policy and a weak **greedy** one. Use
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sampling, or RL from it.
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## Intended use and limitations
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Research artifact for tau-bench agent RL. Not a general assistant, not tuned for safety or
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chat. Its system prompt at training time was the synthetic dataset's own
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`<instructions>/<policy>` wrapper and its tool schemas were the dataset's tau2-style retail
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variant, which differ in three tool names from tau-bench's own retail toolset
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(`cancel_order` vs `cancel_pending_order`, `find_user_by_contact` vs `find_user_id_by_email`,
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`find_user_by_name` vs `find_user_id_by_name_zip`). In practice the model conditions on
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whatever schemas are given in context and transferred to the real tau-bench names with
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essentially no penalty (5 parse failures across 165 eval episodes).
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## License
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Apache-2.0, inherited from both Qwen3-8B and the tau-bench-synthetic dataset.
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