Decision engine
What lifts a score
The one question every model-builder asks: to improve a benchmark, what actually works — and by how much? Pick a target and see the datasets, RL environments, and methods shown to raise it, ranked by effect size, each linked to its evidence. Effect sizes are representative deltas from the cited work — they vary with base model and setup.
+17.0methodChain-of-thought prompting
lifts
GSM8K · Math
— Wei et al. 2022 — eliciting step-by-step reasoning sharply raises math word-problem accuracy in large models. paper ↗ +12.5lifts
GSM8K · Math
— Synthetic grade-school math SFT. paper ↗ +10.0methodVerifier-based best-of-N
lifts
GSM8K · Math
— Cobbe et al. 2021 — train a verifier to rerank sampled solutions. paper ↗ +8.0lifts
GSM8K · Math
— Broad math SFT. +8.0methodSTaR (rationale bootstrapping)
lifts
GSM8K · Math
— Zelikman et al. 2022 — bootstrap reasoning from self-generated rationales. paper ↗ +7.0methodSelf-consistency (sample + vote)
lifts
GSM8K · Math
— Wang et al. 2022 — sample multiple CoT paths and majority-vote the answer. paper ↗