README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.jsonl
      - split: dev
        path: data/dev.jsonl
license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - dpo
  - preference
  - swe-agent
  - code
  - reasoning
size_categories:
  - n<1K

vinhable/vinhable_single

Preference pairs for DPO on multi-turn software-engineering agent trajectories from the Albedo subnet (Bittensor SN97): GLM-5.2 regenerated with its own reasoning vs the reigning King 127 on the same task. This is the single variant (one pair per task: the comparison with the largest score gap); its sibling is vinhable/vinhable_double. Both share the same tasks and the same task-level train/dev split, so they can be compared directly.

	rows	train	dev	pairs	GLM chosen	King chosen	tasks
single	1218	1127	91	214	159	55	218

Median score gap 0.326 (minimum 0.15); mean GLM score 0.696, mean King score 0.483 over the pairs.

Turn groups

Rows follow the turn-group layout of vinhable/gen_vinhable: each side needs at least four assistant turns (0 pairs dropped) and is cut into the same number of behaviour groups, min(6, turns). Unlike gen_vinhable, the first groups of the two sides need not share a behaviour: the sides come from different models and may open differently. A pair yields one row per prefix length k (sample_uid = pair_id#k<k>, prefix_groups, group_count, is_full_trajectory): both sides hold their first k groups and only group k's assistant turns carry loss: true, so each turn is a target exactly once across a pair's rows; a turn's own loss: false (see below) is kept. Rows whose last group has no supervisable turn on a side are dropped (88 rows). Filter is_full_trajectory == true for one trajectory-level row per pair.

How the data was made

• Tasks. Production evaluation samples of King 127 in the graded-judge era (runs finished 2026-09-23 to 09-26), taken from Albedo's public artifacts. 90% were picked where the published GLM-5.2 reference beat both King rollouts by a wide margin, 10% where the King clearly beat every reference run (so that some pairs prefer the King and "looks like GLM" cannot become a shortcut).
• GLM side. GLM-5.2 continues the production prompt turn by turn with reasoning enabled (OpenRouter, provider Baidu). Each turn is stored exactly as a Qwen3.6 candidate writes it: reasoning + \n</think>\n\n + action. Later turns see earlier turns the way production renders history (actions only). Environment observations come from production's simulator with repository grounding (real snapshot of the task's repo; commands run against it where possible). At generation time only, a short instruction appended to the latest observation asked GLM to reason before every turn; it is not in the stored data.
• King side. The King's own two production rollouts of the task, with their production scores.
• Scores. Albedo's graded A-T judge (GLM-5.2, top-20 logprobs) on the task's own published checklist. The King's scores are production's; the GLM rollout was scored locally with the same judge code. 25 pairs have a GLM score read with a whitespace-tolerant logprob alignment (the provider's token stream dropped a space that the content kept; verdict letters are unaffected).
• Pairs. GLM is compared with each King rollout; a comparison qualifies when the gap is at least 0.15. The higher score is chosen. Ties are dropped.

Row schema

Field	Meaning
prompt	production context up to the cut point; every message loss: false
chosen, rejected	continuations: assistant turns and environment observations (role: user)
direction	glm_over_king or king_over_glm
chosen_side, rejected_side	glm or king#r1 / king#r2
glm_score, king_score, margin	scores of the two compared rollouts; margin = GLM - King
king_scores_both_rollouts	both King rollouts' production scores
task_role	main (GLM reference beat the King) or contrast (King beat the reference)
sample_uid, prefix_groups, group_count, is_full_trajectory	position of the row in its pair's group split
chosen_behavior_sequence, rejected_behavior_sequence, aligned_groups, aligned_fraction	behaviour labels of the groups so far
split, task_id, eval_run_id, sample_id, source, sample_phase, question_mode, king_version	provenance
assistant turn: content, loss, think_closed, tokens	text after <think>\n; supervise?; closes </think>; Qwen3.6 tokens incl. `<
assistant turn flags	harness_text (truncation notice), empty_think, over_turn_limit (> 4,096 tokens), nudge_echo (see below)

Loss and rendering contract

• Render every completion turn verbatim as <|im_start|>assistant\n<think>\n + content + <|im_end|>; do not pass it through the chat template.
• When a turn is supervised, its context should hold earlier turns as production renders them: the chat template drops earlier reasoning, so the model never sees its past thinking.
• loss: false on the prompt, observations, harness notices, turns with an empty think block, turns over 4,096 tokens, and nudge_echo turns: GLM turns (254 of
  2875. whose visible answer repeated the headings of the generation-time reasoning instruction.

Thinking audit

	GLM turns	King turns
assistant turns	2875	2837
supervised	2618	2832
close the think block	100.0%	99.7%
empty think block	2	0
median turn length (Qwen3.6 tokens)	521	534

GLM reasons longer than the King on average (about 490 vs 360 reasoning tokens per turn on these tasks).

Caveats

• Small: meant as a signal test before scaling up.
• GLM was scored by a local run of the judge, the King by production; on re-judged King rollouts the local judge differed from production by about 0.06 on average, hence the 0.15 margin.
• Observations are simulated (grounded where the command could be executed on the snapshot).
• In vinhable/vinhable_double, pairs of the same task share the GLM side.