data_15k_dedup_canon_pygments — repo-balanced teacher rollouts (Affine SN120)

14,234 prompt–completion pairs distilled from the published duel artifacts of Affine (Bittensor SN120), rebalanced so no single repository dominates.

Built from corpus epoch 7 (manifest 167085451ab6, 40,335 turns) and all 128 eval artifacts published up to 2026-08-11 — including the epoch 6/7 duels, which sample far more evenly than the early ones.

Fields

field	type	description
prefix	list[{role, content}]	chat history — the prompt
turn_id	string	"{traj_id}:{turn_idx}"; split with rpartition(":")
z_raw	string	teacher reasoning, exactly as sampled
y_raw	string	teacher action — a closed ```bash block
answer_raw	string	undecomposed assistant body — the completion
messages	list[{role, content}]	prefix + the assistant reply, as one transcript

messages is exactly prefix + [{"role": "assistant", "content": answer_raw}], so you can train from that single column. Verified on every row: each starts with a system turn, ends with assistant, and messages[:-1] == prefix.

Answer format

answer_raw (and the final messages turn) is plain text with no <think> channel at all:

THOUGHT: {reasoning}\n\n```bash\n{command}\n```

Reasoning lives in a THOUGHT: prose section. The subnet renders one canonical assistant body across model families so forced-logprob differences reflect the models rather than the rendering; this release strips the think wrapper from it so the target is model-agnostic and needs no special-token handling.

Train via messages

from datasets import load_dataset
ds = load_dataset("vuhaian/data_15k_dedup_canon_pygments", split="train")
# hand ds["messages"] to SFTTrainer and let the chat template render it

Pass add_special_tokens=False when tokenizing if your template already emits BOS-like markers (GLM emits [gMASK]<sop>).

Recovering the exact SN120-scored body

The stored body is not byte-identical to what the validator scored — that form opens with a bare </think>, because gen_prompt() ends the prompt inside an open <think> block. z_raw and y_raw are untouched, so rebuild it with:

z = row["z_raw"].strip()
if z.startswith("<think>"):
    z = z[len("<think>"):]
z = z.replace("</think>", "").strip()
scored_body = "</think>\nTHOUGHT: " + z + "\n\n" + row["y_raw"]

That cleaning matters for ~35% of rows whose z_raw carries sampler debris.

Rebalancing

conan-io/conan is only 2.1% of the corpus by turns, but dominated the raw rollout pool: the duel sampler buckets turns as repo|phase_token, and conan's PR trajectories each become their own stratum (279 of 1,411), so round-robin over strata drew it far more often. Both heads are capped at 5 rows per turn_id; every other repository is carried over untouched.

	rows	share
conan-io/conan	4,017	28.2%
pygments/pygments	1,360	9.6%
all other repos (532)	8,857	62.2%

Within a capped turn the kept rows are the most distinct ones, ranked by rarity of the action y_raw. 534 repositories, 3,218 distinct turns; every row is a unique (turn_id, z_raw, y_raw) triple.

Redundant-reasoning rows removed

The teacher scores every rollout twice: lp_own = lpC(y|z) and lp_empty = lpC(y|None), the per-byte logprob of the same action with and without its own reasoning in context. When lp_own is the lower of the two, the reasoning made its own action less likely — the model reasons in detail about, say, cache internals and then runs a bare ls -la, which looks unlikely given that reasoning but is the obvious default without it.

Rows are kept when lp_own >= 1.02 * lp_empty. Both quantities are negative, so this tolerates a small deficit: reasoning may underperform the empty baseline by up to 2% of it before the row is dropped, which keeps numerical ties while removing the genuinely counterproductive cases. That is 3.9% of the source pool.

Notes before training

• Group by turn_id before splitting train/eval. 14,965 rows cover 3,234 distinct turns, so a random split puts the same prefix on both sides.
• 947 rows have consecutive user turns in the prefix. That is genuine agent data — the harness injects a Format error: Expected exactly 1 action, found 2 correction after a malformed reply. Strict chat templates that require alternating roles will reject these.
• ~0.4% of rows carry a corrupted thought label from the teacher itself (THOught:, THOOTH:, THO</think></think>). Left as-is rather than rewriting teacher output; filter on z_raw.startswith("THO") if it matters.
• Reasoning is in a THOUGHT: prose section rather than inside <think>, because the subnet uses one canonical assistant body across model families so forced-logprob differences reflect the models, not the rendering.

Usage

from datasets import load_dataset

ds = load_dataset("vuhaian/data_15k_dedup_canon_pygments", split="train")
print(ds[0]["messages"][-1]["content"])