vinhable/gen_vinhable

Gen Vinhable — King vs challenger preference pairs

Preference data for DPO on multi-turn software-engineering agent trajectories from the Albedo subnet (Bittensor SN97). Each pair compares two rollouts of the same sample in the same evaluation run: one by the reigning King model and one by the challenger it was evaluated against.

Both sides are Qwen3.6-35B-A3B fine-tunes generated by production with thinking enabled, and both were scored by the same judge on the same checklist. The label is simply which rollout scored higher. Unlike a teacher-vs-model dataset, the two sides therefore think in the same way, so the presence or absence of reasoning cannot become a shortcut for the preference.

Layout

File	Samples	Pairs
train-00000-of-00005.jsonl	9,314	1,570
train-00001-of-00005.jsonl	10,188	1,714
train-00002-of-00005.jsonl	11,997	2,012
train-00003-of-00005.jsonl	17,205	2,873
train-00004-of-00005.jsonl	17,335	2,893
dev.jsonl	7,083	1,188
Total	73,122	12,250

Train and dev are disjoint by task_group. All samples of a pair live in the same file.

Evaluation runs finished between 2026-09-04 and 2026-09-23 and were taken newest first, so the newest Kings dominate:

King version	Pairs
127	2,636
126	2,609
125	2,679
124	1,693
123	1,706
122	927

Preferences: King 6,236, challenger 5,769, tie 245 (2% of pairs).

Pair selection

Every sample has two King rollouts and two challenger rollouts, so four King-vs-challenger pairings. A pairing is eligible when:

• both rollouts were scored;
• each side has at least four assistant turns, split into the same number (4-6) of behaviour groups;
• the first behaviour group matches on both sides;
• the winner is not flagged for amputated thinking (a rejected rollout may be).

Of the eligible pairings, the one with the largest score gap is kept. One side is always the King and the other always the challenger. Each exact context appears once, from its newest run. Tasks used in the authors' local evaluation batch are excluded.

Row schema

Field	Meaning
pair_id, sample_uid	pair id; sample_uid is pair_id#k<prefix_groups>
prompt	shared prefix, every message loss: false
chosen, rejected	the two continuations
preference	king, challenger or tie
chosen_side, rejected_side	which model produced each side
chosen_rollout, rejected_rollout	rollout ids (#r1 / #r2) in the source run
king_score, challenger_score, margin	production scores of the two rollouts and their gap
rejected_amputated_thinking	the rejected rollout was flagged for amputated thinking
eligible_pairings	how many of the four pairings passed the filters
prefix_groups, group_count, is_full_trajectory	turn-split position
chosen_behavior_sequence, rejected_behavior_sequence	per-group behaviour labels
eval_run_id, finished_at, king_version, challenger_uid	provenance of the run
split, sample_id, task_group, source, sample_phase, question_mode, scoring_mode	provenance of the sample

Loss contract

Within a sample, loss: true marks only the assistant turns of the final behaviour group. The prompt, environment observations and earlier groups are unsupervised context, so across the k = 1..K samples of a pair each assistant turn is a target exactly once. Harness-written turns (harness_text: true, a truncation notice) and empty turns are never supervised.

Thinking contract — read before training

Assistant content is the exact text the model generated after production's generation prompt <|im_start|>assistant\n<think>\n: its reasoning, </think>, then the action. Render every completion turn verbatim:

<|im_start|>assistant\n<think>\n + content + <|im_end|>

Do not pass these turns through the chat template. About a quarter of production turns never close the think block (think_closed: false); the template would re-render them with an empty <think>\n\n</think> in front, which is exactly the fingerprint Albedo penalises (a rollout where most turns open with an empty </think> has its score halved). The prompt is ordinary chat history and can go through the template.

Audit of the released data (genesis tokenizer):

	chosen	rejected
turns that close the think block	70.1%	71.0%
turns opening with an empty </think>	0.00%	0.00%
median reasoning length (chars)	516.0	443

The supervised span starts on a token boundary after <think>\n, and </think> inside content encodes as its single special token.

Turn splitting

A pair yields one sample per prefix length k: chosen is the first k behaviour groups of the winning rollout and rejected the first k of the losing one, against the same prompt. From k >= 2 each side carries its own earlier groups as context, so the objective is a contrastive variant of DPO rather than the textbook derivation. Filter to is_full_trajectory == true for one plain trajectory-level pair per sample.

Caveats

• Labels come from production's three-vote binary checklist judge. Small gaps are noisy.
• Kings and challengers are closely related models, so many pairs differ in a few turns only.
• Observations were produced by Albedo's LLM environment simulator, not by executing code.