---
license: other
task_categories:
- other
tags:
- dpo
- preference
- computer-use
- gui-agent
- osworld
size_categories:
- n<1K
---

# DPO-Qwen3-LiteOS-ecb96fbe6

Trajectory-level DPO preference pairs for GUI computer-use agents, built on
`lite.osworld` (`train.perturb`) and tokenized for **Qwen/Qwen3-VL-4B-Instruct**.

Generated at cua-lite commit `ecb96fbe6`.

## What a row is

One **(chosen, rejected) trajectory pair** for the same task, both starting from the
same initial environment state. Trajectory-level DPO scores a whole trajectory as the
sum of its per-action log-probabilities, each action conditioned on its own
protocol-windowed context:

```
S(tau) = sum_t log pi(a_t | h_t),   h_t = f_protocol(x, o_<=t, a_<t)
L_DPO  = -log sigmoid(beta * [ (S_pol(tau+) - S_ref(tau+)) - (S_pol(tau-) - S_ref(tau-)) ])
```

Only assistant action tokens are scored; observations/screenshots are conditioning
context and must be masked out of the loss. **The trainer must SUM `response_tokens`
log-probs across every step of a side** to reconstruct `S(tau)` — averaging per step
yields a different (weaker) objective.

## Schema

| column | type | meaning |
|---|---|---|
| `task_id` | string | shared task; both sides ran the same initial state |
| `margin` | float64 | `pos_return - neg_return` |
| `pos_return`, `neg_return` | float64 | episode returns of each side |
| `processed_images` | large_list[large_binary] | PNG bytes: chosen's images, then rejected's |
| `steps` | list[struct] | chosen steps followed by rejected steps, one per assistant action |
| `n_chosen_steps` | int64 | split point: `steps[:n]` chosen, `steps[n:]` rejected |
| `chosen_metadata`, `rejected_metadata` | string | JSON, source rollout metadata |

Each `steps` entry: `{prompt, image_indices, response, response_tokens, reward, status,
prompt_tokens}`. `image_indices` addresses the concatenated `processed_images`
directly — the rejected side's indices are already offset by the chosen side's image
count. `prompt_tokens` is null for image-bearing steps (vision-token expansion must
happen at train time).

The two sides are concatenated into single `steps`/`processed_images` columns rather
than four `chosen_*`/`rejected_*` columns so the row fits training harnesses that
expose only a few per-row slots to a rollout function.

## Construction

```
collect -> annotate (quality gates -> metadata.others.exclude_reason)
        -> pair (task_id match, margin filter, action-hash dedup)
        -> export (render + tokenize with the target model's adapter)
```

- **chosen**: any trajectory with a clean `exclude_reason` and the higher return
  (GPT-5.5 teacher, or a Qwen3-VL-4B rollout that succeeded)
- **rejected**: Qwen3-VL-4B-Instruct rollouts only — on-policy negatives for that model
- `margin > 0.5` (on this env's 0/1 rewards, "success vs failure")
- at most 4 pairs per task, widest margin first
- trajectories with an identical assistant-action sequence are collapsed before
  pairing, so no duplicate pair inflates one trajectory's gradient weight

## Statistics

| | |
|---|---|
| pairs | 382 |
| distinct tasks | 109 |
| distinct chosen / rejected trajectories | 239 / 224 |
| exact duplicate pairs | 0 |
| cross-model / same-model pairs | 334 / 48 |
| chosen steps (mean / median) | 10.0 / 8 |
| rejected steps (mean / median) | 15.8 / 13 |
| response tokens, chosen / rejected | 219,933 / 352,971 |
| images | 9,850 |

Rejected-side quality tags (why each negative is a negative) are preserved in
`rejected_metadata.others.exclude_reason`: `footgun:loop`, `incomplete`,
`footgun:undo_storm`, etc. A clean tag means the trajectory simply failed the task
checker.

## Known characteristics

- **Length asymmetry**: rejected trajectories carry ~1.6x the response tokens of
  chosen (median per-pair ratio 1.48; 96 of 382 pairs have a *shorter* rejected side).
  Since standard DPO sums rather than length-normalizes, some of the achievable
  margin is attributable to length. Consider tracking premature-termination rate as
  an explicit eval.
- **No `Thought:` channel.** Both sides render as `Action: <text>` + `<tool_call>`.
  The teacher's inline reasoning exists upstream but is not emitted by this adapter
  configuration, so it is absent from *both* sides — symmetric, hence no style
  shortcut, but DPO cannot teach reasoning here.
- 6 chosen trajectories end on a text turn with no tool call (question-answering
  tasks where the teacher replied in prose instead of calling `answer()`).
- `train.perturb` tasks are perturbed variants of `lite.osworld` **eval** tasks.
  Training on this data and evaluating on that eval split is a leakage path.

## Provenance

Teacher rollouts: GPT-5.5. Student rollouts: Qwen/Qwen3-VL-4B-Instruct.
Tokenizer/chat template frozen at export: `Qwen/Qwen3-VL-4B-Instruct` — a set exported
under one model family's adapter cannot train another.