MMODELYST
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.

+17.0
methodChain-of-thought prompting
lifts GSM8K · MathWei et al. 2022 — eliciting step-by-step reasoning sharply raises math word-problem accuracy in large models. paper ↗
+12.5
lifts GSM8K · MathSynthetic grade-school math SFT. paper ↗
+10.0
methodVerifier-based best-of-N
lifts GSM8K · MathCobbe et al. 2021 — train a verifier to rerank sampled solutions. paper ↗
+8.0
lifts GSM8K · MathBroad math SFT.
+8.0
methodSTaR (rationale bootstrapping)
lifts GSM8K · MathZelikman et al. 2022 — bootstrap reasoning from self-generated rationales. paper ↗
+7.0
lifts GSM8K · MathIncludes math SFT. paper ↗
+7.0
methodSelf-consistency (sample + vote)
lifts GSM8K · MathWang et al. 2022 — sample multiple CoT paths and majority-vote the answer. paper ↗
+6.0
lifts GSM8K · MathQuestion-bootstrapped SFT. paper ↗