R0042/2026-03-28/Q003/SRC02
Google DeepMind — Consistency Training for Anti-Sycophancy
Source
| Field |
Value |
| Title |
Consistency Training Helps Stop Sycophancy and Jailbreaks |
| Publisher |
arXiv (Google DeepMind) |
| Author(s) |
Alex Irpan, Alexander Matt Turner, Mark Kurzeja, David K. Elson, Rohin Shah |
| Date |
2025-10-31 |
| URL |
https://arxiv.org/abs/2510.27062 |
| Type |
Academic research paper |
Summary
| Dimension |
Rating |
| Reliability |
High |
| Relevance |
Medium |
| Bias: Missing data |
Low risk |
| Bias: Measurement |
Low risk |
| Bias: Selective reporting |
Low risk |
| Bias: Randomization |
N/A — not an RCT |
| Bias: Protocol deviation |
Low risk |
| Bias: COI/Funding |
Low risk |
Rationale
| Dimension |
Rationale |
| Reliability |
Peer-reviewed research from Google DeepMind with reproducible methodology. |
| Relevance |
Demonstrates anti-sycophancy as an explicit research design goal at a major AI lab. However, this is model provider research, not enterprise customer deployment. |
| Bias flags |
Low risk — academic research with clear methodology. Affiliated with Google but research goals are transparent. |
| Evidence ID |
Summary |
| SRC02-E01 |
Consistency training as anti-sycophancy method from Google DeepMind |