Datasets:
File size: 2,811 Bytes
8dd86f7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | ---
license: apache-2.0
task_categories:
- text-generation
tags:
- agent
- scienceworld
- distillation
- rose
language:
- en
---
# student_partial_sciworld
Partial SciWorld trajectories: **Qwen3-1.7B (base) acting for its first 5 turns** on
the 2,120-task training split. These are the *student prefixes* an online-ROSE run sees
before the teacher takes over — captured separately so the take-over point can be
studied, or teacher continuations generated offline.
## What is here
| | |
|---|---|
| rows | **2,120** (one per training task) |
| file | `student_partial_sciworld.jsonl` (11.4 MB) |
| model | `Qwen/Qwen3-1.7B`, untrained base — the starting point of our ROSE/OPD runs |
| turns | 5 (mean 4.96; a few episodes end early) |
| sampling | temperature 1.0, `max_new_tokens` 512, context 16384 |
| mean score | **0.0352** |
Sampling matches what the student does inside the online-ROSE loop, so these prefixes
are distributionally the same as the ones the teacher continues during training.
## Schema
```json
{
"item_id": "sciworld_0",
"task": "boil",
"var": 0,
"score": 0.0, // ScienceWorld completion in [0,1] after 5 turns
"success": 0, // score == 1
"rounds": 5,
"conversations": [ // system + 24-action instruction + ack, then obs/action pairs
{"role": "user", "content": "You are an agent for science world. ..."},
{"role": "assistant", "content": "OK. I'll follow your instructions ..."},
{"role": "user", "content": "<observation>"},
{"role": "assistant", "content": "Thought: ...\nAction: ..."}
]
}
```
`conversations` is the full AgentGym-protocol dialogue, ready to re-render with any
chat template.
## Why 5 turns
Five turns is where online ROSE cuts. At that point the student has explored but has
essentially completed nothing — mean score **0.0352**, versus **0.0479** when the same
base model runs the full 20 turns. The take-over lands after the student has committed
to an approach but before the episode is decided.
For reference, on the same environment (200 held-out tasks, ≤20 turns, greedy):
| | Score | Success | Pass(>0) |
|---|---|---|---|
| Qwen3-1.7B base | 0.0479 | 0.0000 | 0.1650 |
| SFT on Qwen3-32B trajectories | 0.2077 | 0.0450 | 0.4100 |
| online ROSE (5 student + 5 teacher) | 0.3127 | 0.0600 | 0.6600 |
| Qwen3-32B teacher | 0.4346 | 0.1100 | 0.9350 |
## Environment
ScienceWorld via the AgentGym protocol. Tasks are `(task_type, variation)` pairs with
seven variation families excluded (`5-1 5-2 9-1 9-2 9-3 10-1 10-2`), giving 4,639 games
total; this split holds 2,120 of them. The prompt is the standard 24-action REACT
instruction, and actions are parsed from the first non-empty line after `Action:`.
Scores are ScienceWorld's own completion signal, not a learned reward.
|