Vision-Language-Action (VLA) & Robot Learning Papers — FineSet

A research-paper dataset on Vision-Language-Action (VLA) & Robot Learning Papers, assembled, deduplicated, and quality-scored by FineSet from arXiv and Semantic Scholar.

📸 This is a dated snapshot — generated 2026-06-19. It is not auto-updated. Research on Vision-Language-Action (VLA) & Robot Learning Papers moves fast — new papers land on arXiv every week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. ↓

Why this dataset

• Quality-scored: quality_score float (0–1), blends citations with recency + code/venue signals — filter out the noise
• Papers with code: 96 flagged via has_code — find reproducible work fast
• Deduplicated: arXiv + Semantic Scholar cross-referenced, duplicate records merged
• Clean JSONL: 484 records, one per line, normalized fields — no encoding garbage

Dataset details

• Records: 484
• Date range: 2021–2026
• Snapshot date: 2026-06-19 (frozen — see note above)
• Sources: arXiv, Semantic Scholar (cross-referenced, duplicates merged)
• arXiv categories: cs.RO, cs.LG, cs.CV
• Quality scoring: citations + recency + code/venue blend, 0–1 (p50=0.401, p90=0.65)
• Format: JSONL, one record per line

Fields

Field	Type	Description
id	string	Deterministic SHA256 record id
sources	list	Which sources contributed (arxiv, semantic_scholar)
title	string	Paper title
abstract	string	Full abstract
authors	list	Author names
categories	list	arXiv category codes
fields_of_study	list	Semantic Scholar field tags
published_date	string	ISO 8601 date
url	string	arXiv abstract URL
pdf_url	string|null	Open-access PDF if available
arxiv_id	string|null	arXiv identifier
doi	string|null	DOI if available
citation_count	int	Citation count (Semantic Scholar)
influential_citation_count	int	Influential citations (Semantic Scholar)
has_code	bool	Code repo detected in the arXiv comment
code_url	string|null	GitHub URL if detected
venue	string|null	Publication venue
quality_score	float	0–1, blended (citations + recency + code/venue)

Quality score methodology

quality_score = max(impact, freshness), clamped to [0, 1], where:

• impact = max( log10(citations+1)/4 , log10(influential_citations+1)/2 ) — realized impact (0.5 at 100 citations, ~0.75 at 1,000, 1.0 at 10,000+).
• freshness = recency × (0.35 + 0.30·has_code + 0.20·has_venue) — a baseline for recent papers (so a strong paper published this week isn't scored 0 just for lacking citations), where recency is 1.0 for papers ≤60 days old and decays linearly to 0 by ~18 months.

Old highly-cited papers score on impact; brand-new papers score on freshness; old uncited papers score ~0. Useful for filtering training data by quality, not just age.

👉 Want this on YOUR topic, updated daily?

This snapshot is frozen at 2026-06-19. The live FineSet pipeline keeps a dataset like this refreshed every day on whatever topic you describe — new papers in, dedup and quality scoring automatic, export as JSONL/Parquet or push straight to the Hub.

Tell me the topic you'd want and I'll run the pipeline on it — open a discussion on this dataset, it's free and it's how I decide what to build next.

→ fineset.io — describe what you want to train on, get a dataset. Early-access waitlist open (referral skip available).