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SeanWang0027
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Model Details
Model Provider
SeanWang0027
Model Tree
Qwen/Qwen3-1.7B
Input Modalities
Output Modalities
Supported Functionality
gpt-5.4-mini, OpenAI API, reasoning_effort=medium) continues the same environment for up to 5 turns. Only the
teacher's turns are training targets (cross-entropy); the student's turns and all observations are context. Only the teacher's
visible reply is used.Base Qwen3-1.7B scores 0.12%. Every number is the mean ± std of 4 independent passes at temperature 0.4, 512 tokens per turn, at most 30 rounds, thinking off, no system turn; success = final score 100, Avg@1 = mean final score / 100.
| method | epoch | success rate (avg of 4 passes) | Avg@1 | share of turns that are the literal open/close … | model |
|---|---|---|---|---|---|
| SFT | 1 | not evaluated | — | — | qwen3-1.7b-sciworld-sft-gpt54mini-ep1 |
| SFT | 2 | 13.25% ± 2.56 | 0.1574 | 74.20% | qwen3-1.7b-sciworld-sft-gpt54mini-ep2 |
| SFT | 3 | 12.88% ± 1.24 | 0.1557 | 74.88% | qwen3-1.7b-sciworld-sft-gpt54mini-3ep |
| ROSE 10+5 | 1 | not evaluated | — | — | qwen3-1.7b-sciworld-rose-s10t5-gpt54mini-ep1 |
| ROSE 10+5 | 2 | 7.75% ± 1.92 | 0.1455 | 62.68% | qwen3-1.7b-sciworld-rose-s10t5-gpt54mini-ep2 |
| ROSE 10+5 | 3 | 17.50% ± 2.29 (re-run: 15.38% ± 1.43) | 0.2942 (0.2512) | 35.22% | qwen3-1.7b-sciworld-rose-s10t5-gpt54mini-3ep |
| ROSE 10+5 | 4 | 20.25% ± 2.25 | 0.3433 | 17.98% | qwen3-1.7b-sciworld-rose-s10t5-gpt54mini-ep4 |
| ROSE 10+5 | 5 | 25.75% ± 1.30 | 0.3692 | 2.77% | qwen3-1.7b-sciworld-rose-s10t5-gpt54mini-ep5 |
| ROSE 10+5 | 6 | 20.75% ± 1.82 | 0.2946 | 26.59% | qwen3-1.7b-sciworld-rose-s10t5-gpt54mini-ep6 ← this model |
How to read this table:
gpt-5.4-mini, medium) copies the prompt's action template open/close OBJ literally in 29.89% of its own turns; the
simulator rejects it every time. Students inherit the habit, and their test success tracks how strong the habit is in the
particular checkpoint (last column). In online ROSE the habit oscillates during training, so single-checkpoint numbers are
noisy: ROSE epoch 2 was saved at a relapse peak, epochs 3 and 5 near minima, epoch 6 during another relapse.ReAct. AgentGym's ScienceWorld instruction as a user turn, a canned assistant acknowledgement, then one user turn per observation;
the model replies Thought:\n...\n\nAction:\n<one command>. Render with Qwen3's chat template and enable_thinking=False.
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained(REPO); model = AutoModelForCausalLM.from_pretrained(REPO, torch_dtype="bfloat16")ids = tok.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False, return_tensors="pt")from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained(REPO); model = AutoModelForCausalLM.from_pretrained(REPO, torch_dtype="bfloat16")ids = tok.apply_chat_template(messages, add_generation_prompt=True, enable_thinking=False, return_tensors="pt")