Datasets:

anthonym21
/

rlcd-decision-v1

license: cc-by-4.0
task_categories:
  - text-classification
language:
  - en
tags:
  - reinforcement-learning
  - calibration
  - bandit-feedback
  - decision-making
  - rlcd
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: train.jsonl
      - split: validation
        path: val.jsonl
      - split: test
        path: test.jsonl

RLCD Decision Dataset (v1)

Typed decision questions for training and evaluating models that answer with a calibrated probability distribution over declared options instead of generated text. Built for RLCD — reinforcement learning for calibrated decisions (see also the trained export anthonym21/qwen3-0.6b-rlcd-decision).

Every row is one typed question over a context: a choice question over unordered options, a score question over ordered levels, or a noul (yes/no) question. The RLCD training loop treats each row as a bandit arm — the environment reveals only whether the sampled option was correct, never the label — and the reward r = c - p_a pushes the sampled probability toward calibration.

Schema

field	type	meaning
`primitive`	string	`choice` (unordered), `score` (ordered levels), `noul` (yes/no)
`context`	string	the state / passage the question is about
`question`	string	the question, in words
`choices`	list[string]	the declared options (2–26)
`ordered`	bool	whether the options are ordered levels (score questions)
`answer`	int	index into `choices` of the correct option
`source`	string	which of the 8 sources the row came from
`id`	string	stable row id `<source>-<n>[-<aspect>]`

One JSON object per line; CRLF line endings preserved from the release build.

Splits

split	rows	sources	per-source
train	64,000	8	8,000
validation	8,000	8	1,000
test	8,000	8	1,000

Train primitives: 34,667 choice / 18,667 score / 10,666 noul. Validation and test: 4,334 / 2,333 / 1,333 each. stats.json (included) has the full per-source breakdown.

Provenance

Built with rlcd.data build --per-source 8000 --seed 0 on 2026-09-17 from the code at commit 57a179b. These are the exact files used for every published run in the repo's results tables; a later rebuild produced different bytes, so use these for comparable runs.

Source datasets (question converters in rlcd/data.py):

source	upstream dataset	upstream license	transformation
bitext	bitext/Bitext-customer-support-llm-chatbot-training-dataset	CDLA-Sharing-1.0	intent classification over sampled label subsets
banking77	legacy-datasets/banking77 (mirror of PolyAI/banking77)	CC-BY-4.0	intent classification over sampled label subsets
ag_news	fancyzhx/ag_news	unspecified upstream	topic classification, fixed 4 options
mnli	nyu-mll/multi_nli	CC-BY-3.0 / CC-BY-SA-3.0 (mixed)	premise–hypothesis relation, fixed 3 options
sst5	SetFit/sst5	unspecified upstream (SST derivatives)	5-level sentiment, ordered
yelp	Yelp/yelp_review_full	Yelp Dataset terms	5-level star rating, ordered, text truncated to 1,500 chars
boolq	google/boolq	CC-BY-SA-3.0	yes/no question over passage
triage	synthetic generator in `rlcd/data.py` (original)	—	enterprise support tickets with department / priority / escalation questions

The triage rows are fully synthetic (original work). All other rows are transformed subsets of the upstream datasets above; credit for the underlying texts belongs to the upstream sources, and their terms (some share-alike) apply to those portions. This repo is distributed as CC-BY-4.0 as a convenience tag; if your use is sensitive to the upstream terms, follow the links and check them.

Checksums (md5)

train.jsonl  cdfee4c9792751cf5b22668eb3f9dc33
val.jsonl    4b95911ffc76ed1789f7989f623d0a5a
test.jsonl   15c33b165d70639d8bd7d23d624908f4

Intended use

Research on decision-making LLMs under bandit/outcome-only feedback, probability calibration (ECE, Brier), and confidence-aware classification. Not a benchmark of world knowledge: every split is in-distribution for the sources above and the questions are template-generated.

Citation

@software{maio2026eve_rlcd,
  title = {eve-rlcd: reinforcement learning for calibrated decisions},
  author = {Anthony Maio},
  url = {https://github.com/anthony-maio/eve-rlcd},
  year = {2026}
}