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GPQA Diamond

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2

RESEARCH · 1 SOURCE · The Decoder

Epoch AI: cost to reach fixed benchmark scores fell ~47% per quarter; MIT finds smaller algorithmic gains (~3x/yr)

Epoch AI reports that market prices to achieve fixed scores on five AI benchmarks fell about 47% per quarter (≈13x/year) since 2023, citing examples like OpenAI's o3 vs GPT-5.6; the group warns its sample is limited and measures market price per benchmark score rather than pure algorithmic progress. MIT researchers (Hans Gundlach et al.) using broader model pricing data find 5–10x annual declines in benchmark cost but estimate actual algorithmic efficiency gains of ~3x/year after removing hardware and competition effects, and note some improvements come from higher test‑time compute rather than efficiency.

8.0

RESEARCH · 1 SOURCE · arXiv cs.AI

QVAC Genesis III: 191.43B-token synthetic STEM corpus for efficient LM pre-training

An arXiv preprint introduces QVAC Genesis III, a 191.43B-token, multi-domain synthetic STEM corpus covering 19 domains and multiple difficulty/educational styles; it is generated via a dual teacher‑distillation strategy that uses a weak edge-scale student model to produce corrective explanations and contrastive option-level reasoning, and includes an LLM-as-a-parser evaluation protocol. Controlled from‑scratch experiments with 1.7B-parameter models show consistent improvements over the open synthetic corpus Cosmopedia-v2 and the public Cosmo-1B model on ARC, GPQA Diamond and MMLU STEM benchmarks (up to +28.57% on ARC-E and +21.35% on ARC-C), with Valid Answer Rates up to 99.45%.

6.0