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Nov 5
PapersBuildersSelected14:04
Training Data and Routing Choices Push Open Models on ARC-AGI-2

The clearest lesson is that most of the gain for the 27B model came from the serving-side inference procedure rather than fine-tuning data, which changes how practitioners should budget effort on reasoning benchmarks. Builders running small-model inference on constrained hardware can reuse the expert-pruning and KV-cache techniques directly. The study's 189-run methodology on the ARC Prize Foundation's semi-private set also offers a rare reproducible comparison for offline ARC-AGI-2 solvers.

arXiv cs.AI@Nguyen Minh Thang, Trinh Thi PhuongSource

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