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.

+10.0
methodRLHF (InstructGPT)
lifts IFEval · Language & InstructionOuyang et al. 2022 — RL from human feedback greatly improves instruction following. paper ↗
+9.0
lifts IFEval · Language & InstructionBroad open instruction mixture. paper ↗
+6.0
lifts IFEval · Language & InstructionPreference data improves instruction following. paper ↗
+6.0
methodDirect Preference Optimization (DPO)
lifts IFEval · Language & InstructionRafailov et al. 2023 — preference optimization without a separate reward model. paper ↗
+3.0
lifts IFEval · Language & InstructionReal-world conversational data. paper ↗
+1.5
lifts IFEval · Language & InstructionTool-following overlap. paper ↗