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.
+30.0methodLong reasoning + RL (R1-style test-time compute)
lifts
AIME · Math
— DeepSeek-R1 2025 — long chains-of-thought trained with RL dramatically raise competition-math accuracy. paper ↗ +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 ↗ +14.0lifts
MATH · Math
— Competition math with CoT solutions. +12.5lifts
GSM8K · Math
— Synthetic grade-school math SFT. paper ↗ +12.0methodLong reasoning + RL (R1-style test-time compute)
lifts
GPQA Diamond · Reasoning
— DeepSeek-R1 2025 — reasoning RL transfers to graduate-level QA. paper ↗ +10.0methodRLHF (InstructGPT)
lifts
IFEval · Language & Instruction
— Ouyang et al. 2022 — RL from human feedback greatly improves instruction following. paper ↗ +10.0methodVerifier-based best-of-N
lifts
GSM8K · Math
— Cobbe et al. 2021 — train a verifier to rerank sampled solutions. paper ↗ +9.0lifts
IFEval · Language & Instruction
— Broad open instruction mixture. paper ↗ +9.0lifts
MATH · Math
— Bootstrapped/rewritten math reasoning. paper ↗ +8.0methodProcess reward models (Let's Verify Step by Step)
lifts
MATH · Math
— Lightman et al. 2023 — step-level verifiers beat outcome-only reward on MATH. paper ↗ +8.0methodChain-of-thought prompting
lifts
MATH · Math
— Wei et al. 2022 — step-by-step reasoning transfers to harder math. paper ↗ +8.0lifts
GSM8K · Math
— Broad math SFT. +8.0lifts
HumanEval · Code
— Code instruction tuning. +8.0methodSTaR (rationale bootstrapping)
lifts
GSM8K · Math
— Zelikman et al. 2022 — bootstrap reasoning from self-generated rationales. paper ↗ +7.0lifts
MBPP · Code
— Code instruction tuning. +7.0methodInstruction tuning (Flan / scaling)
lifts
BIG-Bench Hard · Reasoning
— Chung et al. 2022 — strong gains on BIG-Bench Hard. paper ↗ +7.0methodSelf-consistency (sample + vote)
lifts
GSM8K · Math
— Wang et al. 2022 — sample multiple CoT paths and majority-vote the answer. paper ↗ +6.5lifts
AIME · Math
— Olympiad-style transfer. +6.0methodReAct (reason + act with tools)
lifts
WebArena · Agents
— Yao et al. 2022 — interleave reasoning and tool actions for agents. paper ↗ +6.0lifts
IFEval · Language & Instruction
— Preference data improves instruction following. paper ↗ +6.0methodDirect Preference Optimization (DPO)
lifts
IFEval · Language & Instruction
— Rafailov et al. 2023 — preference optimization without a separate reward model. paper ↗ +5.0methodInstruction tuning (Flan / scaling)
lifts
MMLU · Knowledge & QA
— Chung et al. 2022 — scaling instruction-finetuning lifts held-out knowledge tasks. paper ↗ +5.0lifts
HumanEval · Code
— Large permissively-licensed code pretraining corpus. paper ↗ +5.0lifts
MATH · Math
— Transfers partially to competition math. paper ↗ +4.0lifts
MMLU · Knowledge & QA
— Educational-quality pretraining filter. paper ↗ +3.5lifts
HumanEval · Code
— Includes coding instructions. +3.0lifts
IFEval · Language & Instruction
— Real-world conversational data. paper ↗ +2.5lifts
MMLU · Knowledge & QA
— GPT-4-style instruction SFT. +1.5lifts
MMLU · Knowledge & QA
— Small knowledge transfer. paper ↗ +1.5lifts
IFEval · Language & Instruction
— Tool-following overlap. paper ↗