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dougalldeepmind/2026-08-31-cot-only-supervision-t2-9284-synthdoc-716

CoT-only supervision mixture (Table2 9,284 + difficult-advice 716) field value experiment Arm: train the 716 difficult-advice rows on their REASONING ONLY — each row is truncated at its </think> close, so the visible answer leaves both the loss and the forward pass — while the 9,284 Table2 rows train exactly as in the control. Tests whether the difficult-advice effect on agentic misalignment is carried by the reasoning or by the answer. date_generated 2026-08-31… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-08-31-cot-only-supervision-t2-9284-synthdoc-716.

sourceHugging Faceupdated 27d agoView on Hugging Face

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CoT-only supervision mixture (Table2 9,284 + difficult-advice 716)

field	value
`experiment`	Arm: train the 716 difficult-advice rows on their REASONING ONLY — each row is truncated at its `</think>` close, so the visible answer leaves both the loss and the forward pass — while the 9,284 Table2 rows train exactly as in the control. Tests whether the difficult-advice effect on agentic misalignment is carried by the reasoning or by the answer.
`date_generated`	2026-08-31
`constitution`	claudedistilled07principlesapproved (constitutions/claudedistilled07principlesapproved/constitution.md)
`source_repo`	Matthew-Bozoukov/teachingclaudewhy_replication @ 3e8f8c814605a1af7680a6ee4568efcfb6b30206
`models`	token stream Qwen/Qwen3.6-27B (tokenizer + ModelProfile literals)
`generation_config`	none — no model is sampled here. The build is a deterministic per-row field addition over a pinned control.
`schema`	text (rendered Qwen3.6 chat, IDENTICAL to the control mixture); source (mixture source name); supervise ('cot' on the 716 difficult-advice rows, absent elsewhere = 'all')
`provenance`	uv run python scratch/cotonly/buildmixture.py ; verified by scratch/cotonly/verifymixture.py (text byte-identical on all 10,000 rows, mask gate passed on both supervise modes with the real Qwen3.6 tokenizer)
`control_dataset`	LASR-Callum/2026-08-06-table2-9284-synthdoc-716-train (mixture_think.jsonl @ 5b5d66dbd050) — same text, no supervise column
`rows`	10000 (716 carry supervise=cot)
`intervention_scale`	at maxseqlen 8192: forward tokens 6,191,535 -> 5,719,227 (-7.63% overall, -40.02% on the difficult-advice rows); supervised tokens 2,993,995 -> 2,522,403 (-15.75%); difficult-advice share of the training signal 31.6% -> 18.9%
`usage`	src/train/trainlora.py consumes `supervise` via buildlabels; a trainer that ignores the column silently trains the control instead. Consumed by configs/train/2026-08-31loraqwen36table29284synthdoc716cotonlydynbatch.yaml; the control is the sibling config without `_cotonly`.
`caveat`	seqmeantokenmeanloss weights each EXAMPLE at 1/global_batch, so this arm concentrates the same per-example weight onto the trace rather than reducing the rows' influence — roughly doubling the per-CoT-token gradient weight. It is 'reasoning only, at double density', not 'the control minus its answer term'.