---
title: "Inverse Scaling Prize"
type: "entity"
entity_kind: "concept"
status: "hub"
canonical_name: "Inverse Scaling Prize"
aliases: ["Inverse Scaling"]
first_seen: "2026-10-23T00:00:00.000Z"
writer_model: "claude-sonnet-5"
connects_to: ["inverse scaling","distractor task","task-taxonomy","U-shaped scaling","Jason Wei"]
seek_code_commit: "d40ee39"
---


A two-round public contest (organized by McKenzie, Lyzhov, Pieler, Parrish, Bowman, Perez and collaborators, 2022–2023) that offered prize money for finding tasks on which large-language-model performance gets *worse*, not better, as model scale increases. The contest produced a fixed set of 11 winning tasks — a reusable dataset (inversescaling.com/data) that recurs across multiple independent papers rather than being tied to one.

Matters to this vault as the shared empirical anchor for a small cluster of task-taxonomy papers: [[claim-mckenzie-2023-inverse-scaling-four-causal-categories|McKenzie et al. (2023)]] classified the 11 tasks into four causal categories, and [[claim-wei-2023-inverse-scaling-becomes-u-shaped-at-larger-scale|Wei, Kim, Tay & Le (2023)]] re-ran the identical 11 tasks at larger scale and found most of them reverse into U-shaped curves — two papers studying the same dataset reaching a scale-dependent, not fixed, verdict on it.

## References
- [[claim-mckenzie-2023-inverse-scaling-four-causal-categories]]
- [[claim-wei-2023-inverse-scaling-becomes-u-shaped-at-larger-scale]]
- Captures: 10-inbox/raw/2026-10-23-does-published-ml-research-go-further-than-hu.md