build-chain-of-thought-supervision-rules-api-calls TEC-0244 · proposedBuild chain-of-thought supervision from rules, not API calls
Generate reasoning traces for fine-tuning by reverse-engineering templated explanations that match extracted temporal features to candidate-model categories under known oracle labels, instead of querying a large model for the rationale.
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.
Generate reasoning traces for fine-tuning by reverse-engineering templated explanations that match extracted temporal features to candidate-model categories under known oracle labels, instead of querying a large model for the rationale. Filed by the drafting stage from REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting (2608.10149) 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 MSE across eight benchmarks after SFT: Rule-CoT 0.1210 vs GPT-CoT 0.1260 on the small-model candidate group (better on 6/8 datasets) and 0.1455 vs 0.1456 on the foundation-model group; ablating CoT raised MSE from 0.1210 to 0.1315 in-distribution and from 0.1719 to 0.2675 out-of-distribution.. 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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