data_10k_dedup_canon — rebalanced teacher rollouts (Affine SN120)

10,278 prompt–completion pairs, rebalanced from vuhaian/25k_from_rollouts to reduce single-repo dominance.

Why rebalance

In the 25k set conan-io/conan was 65.4% of all rows — not because it dominates the corpus (it is 2.8% of turns) but because the duel sampler buckets turns as repo|phase_token, and conan's PR trajectories each become their own stratum (279 of 1,140). Round-robin over strata therefore drew conan ~25× more often than equal-sized repos.

conan-io/conan is capped at 2 rows per turn_id; every other repository is carried over untouched.

	before	after
total rows	24,930	10,278
conan-io/conan	16,307 (65.4%)	1,655 (16.1%)
all other repos	8,623	8,623

Within a capped turn the kept rows are the most distinct ones, ranked by rarity of the action y_raw, so a cap of 2 keeps two different teacher approaches instead of two copies of one command. This cut same-action pairs from 203 (naive first-2) to 88.

Fields

Identical to the parent dataset:

field	type	description
prefix	list[{role, content}]	chat history — the prompt
turn_id	string	"{traj_id}:{turn_idx}"
z_raw	string	teacher reasoning, as sampled
y_raw	string	teacher action — a closed ```bash block
answer_raw	string	canonical assistant body — the completion

answer_raw is </think>\nTHOUGHT: {z}\n\n{y}, the exact body the validator builds in inject_prompt() and scores through force_text(). See the parent dataset card for the full derivation and caveats.

Known skew — read before training

Capping conan promoted the next-largest repo rather than producing a flat distribution:

repository	rows	share	rows/turn
pygments/pygments	4,623	44.98%	16.2
conan-io/conan	1,655	16.10%	2.0
jawah/charset_normalizer	525	5.11%	9.4
bottlepy/bottle	249	2.42%	5.1
google/textfsm	246	2.39%	5.7

pygments/pygments is now the dominant repo at 45%. It was left untouched by design (the cap targets conan only), and it carries 16.2 rows per turn against conan's capped 2.0. If you want a broadly balanced set, apply the same per-turn_id cap across all repositories rather than conan alone.

180 repositories total; 1,939 distinct turns (unchanged from the parent — capping removes duplicate rollouts, not turns).

Group by turn_id before splitting train/eval, or the same prefix appears on both sides.

Usage

from datasets import load_dataset

ds = load_dataset("vuhaian/data_10k_dedup_canon", split="train")

def to_text(row, tokenizer):
    prompt = tokenizer.apply_chat_template(
        row["prefix"], tokenize=False, add_generation_prompt=True
    )
    if not prompt.rstrip().endswith("<think>"):
        prompt += "<think>"
    return {"prompt": prompt, "completion": row["answer_raw"]}