DPO-Qwen3-2B-LiteOS

Trajectory-level DPO preference pairs for desktop computer-use agents, built on Lite.OSWorld train.perturb. Chosen trajectories come from a GPT-5.5 teacher and from the student's own successes; rejected trajectories are Qwen3-VL-2B-Instruct rollouts on the same task and initial state.

Ablation dataset. The 2B student was used to probe the pipeline; the primary DPO runs use larger students.

Format

One row per preference pair. The two sides are concatenated into single steps / processed_images columns and split by n_chosen_steps:

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
processed_images	large_list[large_binary]	PNG bytes — chosen's images then rejected's
steps	list[struct]	one entry per assistant action; steps[:n] chosen, steps[n:] rejected
n_chosen_steps	int64	the split point n
chosen_metadata, rejected_metadata	string	JSON, source rollout metadata

Each step struct is {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.

Prompts are rendered with the Qwen3-VL chat template and history protocol (full_history_size=4: the last 4 turns keep screenshots, older turns collapse into a text summary), so each step's context is what the model actually sees at that decision point.

Objective

The per-step decomposition is what trajectory-level DPO needs:

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-)) ])

Sum response_tokens log-probs across every step on a side to get that side's S(tau). Observations, screenshots and history summaries are conditioning context only and must be masked out of the loss; only assistant-action tokens are scored.

Construction

collect -> annotate -> stage -> pair -> export. Pairs are drawn from one pooled candidate set: any (chosen, rejected) sharing a task_id whose return margin clears 0.5, capped at 4 pairs per task (widest margin first), with rejected restricted to Qwen/Qwen3-VL-2B-Instruct. A trajectory carrying any exclude_reason quality tag is never chosen but may be rejected. Candidates with identical action sequences within a task are collapsed before pairing, so no two rows share the same (chosen, rejected) content.

Statistics

pairs	408
distinct chosen / rejected trajectories	258 / 236
exact duplicate pairs	0
steps per side (mean)	chosen 10.0, rejected 13.1
response tokens	chosen 232k, rejected 311k (1.34x)
pairs where rejected is shorter	149
images	9408

Known limitations

• Length asymmetry. Rejected sides carry 1.34x the response tokens overall (median per-pair ratio 1.26). Standard DPO uses summed log-probs, so this biases toward shorter outputs; 149 pairs run the other way, which softens it but does not remove it. Length normalisation or per-step averaging are separate variants and should be reported explicitly if used.
• Malformed finish actions on the rejected side. 25 rejected steps call a bare {"name": "terminate"} instead of the declared computer_use(action="terminate") — a real 2B failure mode, preserved verbatim. Rows where the chosen side did this were dropped (424 -> 408), so positives never demonstrate an undeclared tool.
• No Thought channel. The teacher recipe collects grounded inline reasoning, but the Qwen3-VL adapter renders the 2-part wire format (Action: + <tool_call>) and drops it. Both sides are affected identically, so no style shortcut is introduced — but preference signal covers actions only, not reasoning.
• Not independent samples. Pairs are drawn from a smaller set of trajectories than the pair count suggests; a trajectory reused across several pairs receives proportionally more gradient weight.
• Task/eval overlap. train.perturb tasks are perturbed variants of Lite.OSWorld eval tasks. Training on this data and evaluating on that eval split is leakage-affected.