README.md

metadata

pretty_name: Simulated Scholar Search (S3) RL Dataset
license: apache-2.0
language:
  - en
task_categories:
  - question-answering
size_categories:
  - 10K<n<100K
tags:
  - agentic-rl
  - deep-research
  - literature-search
  - reinforcement-learning
  - scientific-literature
  - simscholar
  - s2orc
  - synthetic-data
configs:
  - config_name: default
    data_files:
      - split: train
        path: trainer_13k.jsonl

S3 RL Dataset

Synthetic literature-search questions for agentic reinforcement learning.

Code · S3 collection · Source corpus

The S3 RL dataset contains 13,000 synthetic, single-hop questions for training and analyzing scientific-literature search agents. Each question is grounded in one paper from a fixed corpus of approximately 1.12 million computer-science papers.

At a glance

Property	Value
Rows	13,000
Language	English
Task	Tool-assisted scientific-literature QA
Scope	Single-hop; one source paper per question
Format	JSON Lines

Composition

Answer type	Rows
Abstract	5,000
Value	5,000
Identity	3,000

Query style	Rows
Content conjunction	3,000
Context conjunction	3,000
Detail cue	3,000
Named	2,000
Paraphrastic	2,000

Schema

Field	Type	Description
sample_id	string	Stable generated-item identifier
query	string	Question presented to the agent
gold_answer	string	Reference answer used for grading
anchor	string	Query framing used during synthesis
answer_type	string	abstract, value, or identity

Load the dataset

from datasets import load_dataset

dataset = load_dataset("trillionlabs/SimScholar-RL", split="train")
print(dataset[0])

Research use

The dataset is intended for research on literature-search agents, retrieval-grounded QA, tool-use policies, curriculum design, and agentic reinforcement learning.

Limitations

• Questions and reference answers are synthetic and may contain generation or grading errors.
• The source corpus covers computer science rather than the full scientific literature.
• A fixed local retrieval environment differs from live scholarly search.
• Some questions may be answerable from model memory without tool use.
• Verbatim overlap with source-paper text has not been exhaustively audited.

License

This dataset is licensed under the Apache License 2.0. Third-party paper text, excerpts, and metadata retain their original rights and terms; the Apache license does not supersede them.