Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes explores This research explores effective RLVR fine-tuning strategies for small language models in low-data environments, demonstrating improved sample efficiency and generalization through procedural datasets.. Commercial viability score: 4/10 in LLM Fine-tuning.
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This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Page Freshness
Canonical route: /paper/learning-from-less-measuring-the-effectiveness-of-rlvr-in-low-data-and-compute-regimes
Page-specific freshness sourced from this paper's evidence receipt and score bundle.
Agent Handoff
Canonical ID learning-from-less-measuring-the-effectiveness-of-rlvr-in-low-data-and-compute-regimes | Route /paper/learning-from-less-measuring-the-effectiveness-of-rlvr-in-low-data-and-compute-regimes
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/learning-from-less-measuring-the-effectiveness-of-rlvr-in-low-data-and-compute-regimesMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.18381"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes",
"normalized_query": "2604.18381",
"route": "/paper/learning-from-less-measuring-the-effectiveness-of-rlvr-in-low-data-and-compute-regimes",
"paper_ref": "learning-from-less-measuring-the-effectiveness-of-rlvr-in-low-data-and-compute-regimes",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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