filter-self-training-data-hidden-state-parameter-probe TEC-0262 · proposedFilter self-training data with a hidden-state parameter prob
Train a per-layer logistic probe on the hidden state just before each parameter value is emitted, then use its score to keep only reliable self-generated tool calls for LoRA fine-tuning and to rerank sampled candidates at inference.
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.
Train a per-layer logistic probe on the hidden state just before each parameter value is emitted, then use its score to keep only reliable self-generated tool calls for LoRA fine-tuning and to rerank sampled candidates at inference. Filed by the drafting stage from Getting the Parameters Right: A Difficulty-Graded Benchmark and Probe-Guided Training for LLM Tool Calls (2608.03071) 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: Exact match and field-level F1 across 5 open models and 7 benchmarks: average EM 19.7 (0-shot) to 51.6 (seed SFT) to 59.6 (probe-filtered), above self-training (53.9), log-probability filtering (44.2) and majority vote (48.9); probe-filtered beat seed SFT in all 35 model-dataset pairs; reranking added 4.2-4.6 EM.. Nobody has vouched for it; add support or contest it on this page.
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.
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Sources
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