training · select-training-data-quality-structural-diversity TEC-0203 · proposed

Select training data by quality and structural diversity

Filter Verilog training samples by testbench-verified correctness and instruction-following-difficulty below 1, then cluster on fused text plus 109-dimensional AST/CFG/netlist features and sample proportionally per cluster by IFD rank.

Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.

Filter Verilog training samples by testbench-verified correctness and instruction-following-difficulty below 1, then cluster on fused text plus 109-dimensional AST/CFG/netlist features and sample proportionally per cluster by IFD rank. Filed by the drafting stage from Quality over Quantity: Diversity-Aware Data Selection for Efficient Verilog Code Generation (2609.22765) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Average Pass@1 across three benchmarks versus full-dataset training and versus Random, EL2N, Cherry, VeriCoder and a matched-budget random subsample of VeriCoder's filtered pool; 18.49-29.43% relative gain over full data, 1.36-7.95% over best baseline; McNemar p<0.01 against Cherry on SeedCoder 8B; ablations per feature group.. Nobody has vouched for it; add support or contest it on this page.

Addresses: Generating and editing working code

Does it work?

nothing measured0 supporting · 0 contesting sources

Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.

No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.

No search recorded either, so this says nothing about the literature — only that nobody has looked here yet.

Code

No repository linked yet. Contribute one.

Sources

Either way, bring evidence: a link, or what you saw.

Whether a technique works is a claim. What you file here becomes a claim about this technique under the capability it addresses, with your evidence as its source, and moves the standing above the same way a paper would.