vinhable/sgp_vinhable

SGP Vinhable — C3-Q preference pairs

Preference data for DPO on multi-turn software-engineering agent trajectories. Each row compares two continuations of the same prompt: a GLM-5.2 reference trajectory ("teacher") and an Albedo King rollout, both continuing from the same cut point in the same repository.

This release replaces the earlier supervised fine-tuning export. The previous contents remain reachable through the repository history at commit cd5e8ca5.

Layout

File	Rows	Pairs
train-0000{0..4}-of-00005.jsonl	65,990	11,015
dev.jsonl	7,414	1,237
Total	73,404	12,252

Train and dev are disjoint by task_group. Shards are balanced by bytes, and all samples of a pair live in the same shard.

Row schema

Field	Meaning
pair_id, sample_uid	trajectory id; sample_uid is pair_id#k<prefix_groups>
prompt	shared prefix, every message loss: false
chosen, rejected	the two continuations
preference	teacher, king or tie
chosen_side, rejected_side	which model produced each side
teacher_score, king_score, margin	checklist scores, when measured
label_source	measured_onepass, measured_exact_paired or legacy_teacher_default
label_confidence	high, low or tie
prefix_groups, group_count, is_full_trajectory	turn-split position
chosen_behavior_sequence, rejected_behavior_sequence	per-group behaviour labels
aligned_groups, aligned_fraction	how many groups share a behaviour
split, sample_id, task_group, source, sample_phase	provenance

Loss contract

Within a sample, loss: true marks only the assistant tokens of the final behaviour group. Everything else is unsupervised context: the prompt, the environment observations, and the earlier groups carried along as history. Across the k = 1..K samples of a pair, each assistant turn is a training target exactly once, so no part of the trajectory is weighted more heavily than another.

Scoring every group of every prefix instead would weight group 1 K times and group K once, which is backwards for this task: the later groups are where the model has to have understood and fixed the defect rather than still be reading the repository.

A trainer must honour this flag. Scoring observation tokens, or the context groups, corrupts the objective.

Turn splitting

A pair yields one sample per prefix length k: chosen is the first k behaviour groups of the winning side and rejected is the first k of the losing side, against the same prompt. Groups are contiguous runs of assistant turns sharing a function (inspect_diagnose, reproduce_test, edit, verify_inspect, verify_test, finalize, reason_plan), 4 to 6 per side, equal on both sides.

At k = 1 both sides condition on the same prompt, so that sample is a preference pair in the textbook sense. From k >= 2 each side carries its own earlier groups as context, so the two sides no longer share a state. This is deliberate, and it makes the objective a contrastive variant rather than the original DPO derivation: it raises the likelihood of the better group given the better history and lowers the worse group given the worse history, each normalised against the reference model on its own context.

The alternative, pairing group k of one side against group k of the other under a single shared prompt, is not available here: after the first differing action the two sides are in different environment states, and only 83 of 17,819 candidate pairs share an identical first assistant turn.

Filter to is_full_trajectory == true for one sample per pair, which is a plain trajectory-level preference pair.

Selection

• The first behaviour group must match on both sides, so the two are comparable while still reading the repository. Later groups are free to diverge; that is where the useful difference lives.
• Legacy sota_anchored pairs keep teacher as chosen and are tagged label_confidence: low.
• Legacy pairs whose teacher side shows a known failure mode are dropped: four or more consecutive turns spent on the environment, or a continuation that never runs a test and never reads a diff. 3,020 pairs were removed this way.
• Ties are kept at 5% of pairs.

Label provenance, and what to trust

label_source	Pairs	What the label is
measured_onepass	5,893	teacher and King judged on the same frozen checklist
measured_exact_paired	387	same, from an earlier exact paired evaluation
legacy_teacher_default	5,972	assumed, not measured — see below

Legacy pairs come from the older sota_anchored protocol, where the evaluator scored the reference on a provisional checklist, stored that vote as reference_self_score, then pruned away every question the reference answered "no". The teacher's stored score is therefore 1.0 by construction in most of these rows, and no teacher score on the final checklist exists. Teacher is chosen in this tier by assumption.

The checklists themselves are generated from GLM reference trajectories, which tilts them toward the teacher. A checklist-free head-to-head judge run on 100 pairs found that when the checklist says King won, an independent judge agrees 100% of the time, whereas when the checklist says teacher won the independent judge usually calls it a tie. The 1,558 King-chosen pairs are therefore the most reliable signal here.

Length is close to a proxy for model identity in this corpus: teacher continuations average roughly 6k characters and King roughly 13k regardless of which one wins. Consider a length-normalised objective, or balancing chosen_side, so the model does not learn verbosity as the preference.

Labels are measured on the complete trajectory, so they are weakest at small k, where both sides are usually still inspecting. Down-weighting or dropping prefix_groups <= 2 is reasonable.

Composition

Preference	Pairs
teacher	10,081
king	1,558
tie	613

Sources: mini-coder, open-swe-traces, swe-hero, mini-coder-rs. Phases: cold, pre_edit, at_edit.