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.
+10.0methodRLHF (InstructGPT)
lifts
IFEval · Language & Instruction
— Ouyang et al. 2022 — RL from human feedback greatly improves instruction following. paper ↗ +9.0lifts
IFEval · Language & Instruction
— Broad open instruction mixture. 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 ↗ +3.0lifts
IFEval · Language & Instruction
— Real-world conversational data. paper ↗ +1.5lifts
IFEval · Language & Instruction
— Tool-following overlap. paper ↗