(Mis)generalization of Helpful-Only Fine-tuning

·LessWrong··

TLDRWe study the shortcomings of existing helpful-only models. We find that some show emergent misalignment, others have residual refusal behaviors, and most show poor steerability, sycophancy, and incoherent character. None of these problems are a necessary consequence of helpful-only training, though: we show that synthetic document fine-tuning and adding character-related questions to SFT and RL can mitigate them.Research done as part of MATS/Anthropic Fellows Program. See here for the full p...

Read full article →

Related Articles

US bans differential privacy in Census data
nl · Hacker News · 2h ago
Arch Linux Now Believes Malware Incident Under Control: More Than 1,500 Packages
qwertox · Hacker News · 4h ago
Twenty One Zero-Days in FFmpeg
redbell · Hacker News · 18h ago
CRISPR tech selectively shreds cancer cells, including "undruggable" cancers
gmays · Hacker News · 1d ago
Kimi K2.7-Code: open-source coding model with better token efficiency
nekofneko · Hacker News · 1d ago