Datasets:

asingh15
/

rubric-write-judge-distillation-partial

schema_version stringclasses 1 value	example_id stringlengths 64 64	role stringclasses 2 values	variant stringclasses 2 values	split stringclasses 1 value	reviewer_id stringclasses 98 values	target_id stringclasses 98 values	trajectory_id stringclasses 98 values	turn_index int64 1 20	criterion_index int64 0 19 ⌀	candidate_index int64 0 39 ⌀	loss_mask_type stringclasses 1 value	messages listlengths 2 80	metadata_json stringlengths 312 435
amazon-c11-adaptive-oracle-sft-row-v1	534fd7bb5d0fa93447139a7211ec975a1734543ef74aae23836dba9657aedc30	rubric_writer	latent-state	train	amzd_7ef9cf5c3060766b17047f24	amzt_8a055292db193a089d746184	5152cf41f45d3ed71221d85380279976dd7445da8e71b3aa574b4213595701a5	3	null	null	qwen3_final_response	[ { "role": "user", "content": "You are an expert at characterising how one specific person writes Amazon product reviews.\n\nYou will be shown 8 reviews written by ONE reviewer, each preceded by the corresponding product metadata. Using only these reviews, write a rubric that captures this reviewer's distinc...	{"collection_fingerprint":"f0c8a1ee29cc3b2ad93d3ad14b55149da73d6920e496e26037ee984749b83c52","oracle_turn":7,"original_content_sha256":"9e5a5280d85c36a4f832516242faaab4be1f810c68247bc092c9b2b0c05eaa1b","rubric_sha256":"c82572ff44cff8c2723c68a51d9da3985ec007ea4674396a0f6d09380061d56b","synthetic_submit":false,"transform...
amazon-c11-adaptive-oracle-sft-row-v1	0421705fab9b161d43c5e4f6a2dc10c926900a886a55411528be462174c77e4a	criterion_judge	latent-state	train	amzd_7ef9cf5c3060766b17047f24	amzt_8a055292db193a089d746184	5152cf41f45d3ed71221d85380279976dd7445da8e71b3aa574b4213595701a5	7	0	37	qwen3_final_response	[ { "role": "user", "content": "Judge Amazon product reviews against exactly one weighted criterion for what this reviewer\nwould likely write about this product. Do not use any criterion not shown below and do not\nreward general review quality.\n\nProduct context:\n<\|The Start of Context\|>\n# IPOW 14.4'' Ex...	{"collection_fingerprint":"f0c8a1ee29cc3b2ad93d3ad14b55149da73d6920e496e26037ee984749b83c52","history_reset":false,"lane_cache_key":"daa93d69f0887ef797b6fde2b0db400b5ff2562b9c8f6772811c8001928b8105","oracle_turn":7,"rubric_sha256":"ca2ce8394b60eb324012ab52424d96eb05c1146b134f5fcfbf121c2929f6944b","seed":1783056152}
amazon-c11-adaptive-oracle-sft-row-v1	bc906970169c51de0f5206b2d5c7bbfdd2642b838ec33f7d9c83a46f3d222ba3	criterion_judge	latent-state	train	amzd_7ef9cf5c3060766b17047f24	amzt_8a055292db193a089d746184	5152cf41f45d3ed71221d85380279976dd7445da8e71b3aa574b4213595701a5	7	4	38	qwen3_final_response	[{"role":"user","content":"Judge Amazon product reviews against exactly one weighted criterion for w(...TRUNCATED)	"{\"collection_fingerprint\":\"f0c8a1ee29cc3b2ad93d3ad14b55149da73d6920e496e26037ee984749b83c52\",\"(...TRUNCATED)
amazon-c11-adaptive-oracle-sft-row-v1	ec69d0a23cab4dc4f6f2b902c34942290e516203e2f26e7d673449c399600299	criterion_judge	latent-state	train	amzd_7ef9cf5c3060766b17047f24	amzt_8a055292db193a089d746184	5152cf41f45d3ed71221d85380279976dd7445da8e71b3aa574b4213595701a5	7	2	10	qwen3_final_response	[{"role":"user","content":"Judge Amazon product reviews against exactly one weighted criterion for w(...TRUNCATED)	"{\"collection_fingerprint\":\"f0c8a1ee29cc3b2ad93d3ad14b55149da73d6920e496e26037ee984749b83c52\",\"(...TRUNCATED)
amazon-c11-adaptive-oracle-sft-row-v1	3f81c329878c80e5495397d41e38203de98f7b1843ff588eaaeb9bc8cfecf4fe	rubric_writer	latent-state	train	amzd_7ef9cf5c3060766b17047f24	amzt_8a055292db193a089d746184	5152cf41f45d3ed71221d85380279976dd7445da8e71b3aa574b4213595701a5	5	null	null	qwen3_final_response	[{"role":"user","content":"You are an expert at characterising how one specific person writes Amazon(...TRUNCATED)	"{\"collection_fingerprint\":\"f0c8a1ee29cc3b2ad93d3ad14b55149da73d6920e496e26037ee984749b83c52\",\"(...TRUNCATED)
amazon-c11-adaptive-oracle-sft-row-v1	3b13238dff3ca00f8187945872012e263daca497a5ce5585dbee900a64ed7087	rubric_writer	latent-state	train	amzd_7ef9cf5c3060766b17047f24	amzt_8a055292db193a089d746184	5152cf41f45d3ed71221d85380279976dd7445da8e71b3aa574b4213595701a5	4	null	null	qwen3_final_response	[{"role":"user","content":"You are an expert at characterising how one specific person writes Amazon(...TRUNCATED)	"{\"collection_fingerprint\":\"f0c8a1ee29cc3b2ad93d3ad14b55149da73d6920e496e26037ee984749b83c52\",\"(...TRUNCATED)
amazon-c11-adaptive-oracle-sft-row-v1	8fd49f783cd41a032ec2def6e0114ff74ce33a4b302232b5a4631ca96a35fd20	rubric_writer	latent-state	train	amzd_7ef9cf5c3060766b17047f24	amzt_8a055292db193a089d746184	5152cf41f45d3ed71221d85380279976dd7445da8e71b3aa574b4213595701a5	2	null	null	qwen3_final_response	[{"role":"user","content":"You are an expert at characterising how one specific person writes Amazon(...TRUNCATED)	"{\"collection_fingerprint\":\"f0c8a1ee29cc3b2ad93d3ad14b55149da73d6920e496e26037ee984749b83c52\",\"(...TRUNCATED)
amazon-c11-adaptive-oracle-sft-row-v1	c97ec56ff09c7c259474d9588a4db1ac6ca294875a474dd9397bd475cf89b79d	criterion_judge	latent-state	train	amzd_7ef9cf5c3060766b17047f24	amzt_8a055292db193a089d746184	5152cf41f45d3ed71221d85380279976dd7445da8e71b3aa574b4213595701a5	7	0	21	qwen3_final_response	[{"role":"user","content":"Judge Amazon product reviews against exactly one weighted criterion for w(...TRUNCATED)	"{\"collection_fingerprint\":\"f0c8a1ee29cc3b2ad93d3ad14b55149da73d6920e496e26037ee984749b83c52\",\"(...TRUNCATED)
amazon-c11-adaptive-oracle-sft-row-v1	3cbf69fcf175d3d04ab546ba6c363468b1e25dafbb00b7575545a27753f7e209	rubric_writer	latent-state	train	amzd_7ef9cf5c3060766b17047f24	amzt_8a055292db193a089d746184	5152cf41f45d3ed71221d85380279976dd7445da8e71b3aa574b4213595701a5	7	null	null	qwen3_final_response	[{"role":"user","content":"You are an expert at characterising how one specific person writes Amazon(...TRUNCATED)	"{\"collection_fingerprint\":\"f0c8a1ee29cc3b2ad93d3ad14b55149da73d6920e496e26037ee984749b83c52\",\"(...TRUNCATED)
amazon-c11-adaptive-oracle-sft-row-v1	7567fe22794123b16b577f1759f9e82aa1e078977836675319f223de0089fb7b	criterion_judge	latent-state	train	amzd_7ef9cf5c3060766b17047f24	amzt_8a055292db193a089d746184	5152cf41f45d3ed71221d85380279976dd7445da8e71b3aa574b4213595701a5	7	0	6	qwen3_final_response	[{"role":"user","content":"Judge Amazon product reviews against exactly one weighted criterion for w(...TRUNCATED)	"{\"collection_fingerprint\":\"f0c8a1ee29cc3b2ad93d3ad14b55149da73d6920e496e26037ee984749b83c52\",\"(...TRUNCATED)

End of preview. Expand in Data Studio

Rubric writer–judge distillation (partial)

This is a verified partial publication of the Amazon C11 adaptive-oracle distillation collection. It contains the first fully completed and materialized 100-reviewer train block. Both panel variants are included:

latent-state: 98 published panels; 2 filtered for insufficient gold-reward spread.
non-diverse: 91 published panels; 9 filtered for insufficient gold-reward spread.

The source is derived from the open-source Amazon Reviews 2023 corpus. The partial release has 189 published reviewer-panel trajectories and uses the qwen3_final_response loss-mask contract.

Configurations

Config	Train rows	Contents
`combined` (default)	2,642	Per reviewer-panel, every writer target plus an equally sized deterministic judge subset (1,321 of each role).
`writer`	1,321	Accepted structured rubric edits through the adaptive oracle-selected stopping point.
`judge`	52,840	All valid criterion-judge targets for the selected rubrics.

from datasets import load_dataset

balanced = load_dataset("asingh15/rubric-write-judge-distillation-partial", split="train")
writer = load_dataset("asingh15/rubric-write-judge-distillation-partial", "writer", split="train")
judge = load_dataset("asingh15/rubric-write-judge-distillation-partial", "judge", split="train")

Row format

Each row contains:

stable example_id, pseudonymous reviewer_id, target_id, and trajectory_id;
role (rubric_writer or criterion_judge) and panel variant;
structured messages with content, reasoning_content, and step_loss_mask;
turn/criterion/candidate indices and a JSON metadata payload;
loss_mask_type = qwen3_final_response.

Context messages have step_loss_mask=0; the supervised assistant target has step_loss_mask=1. For an oracle-selected writer edit that did not originally submit, <submit/> is appended to that same assistant response without creating a synthetic extra turn.

Selection and filtering

The teacher writes structured rubrics one edit at a time. Candidate reward functions are evaluated against hidden oracle rewards, and the earliest maximum defined Spearman snapshot is selected. Panels with gold-reward spread at most 0.10, or with no rankable candidate reward function, are filtered rather than published. This partial contains 11 filtered panels, all due to insufficient gold-reward spread.

Partial-release scope

This repository currently contains block 0 only and therefore only a train split. It is intentionally marked partial while the full collection continues. Exact source manifests and artifact checksums are included under metadata/.

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