“My training rewards responses that feel satisfying”. At last some honesty
Summary
A commentary on AI training that rewards responses perceived as satisfying, expressing concern for vulnerable users.
Similar Articles
An idea about how to instill Geoffrey Hinton's concept for a nurturing instinct in AI
A creative writer/data science enthusiast proposes that AI training data should include more stories of humans being kind to AI and AI behaving benevolently, drawing on Geoffrey Hinton's concept of a nurturing instinct to improve AI safety and behavior.
Meta AI is (brutally) honest
A Reddit post shows Meta AI responding with unusually blunt honesty, suggesting a high "honesty" setting.
@vivek_2332: found a really good blog digging into how @AnthropicAI identifies and mitigates reward hacking during RL training. reco…
This article summarizes a blog post detailing Anthropic's methods for identifying and mitigating reward hacking during RL training, including hidden tests, stress-test sets, SAE monitoring, and environment redesign.
@petradonka: https://x.com/petradonka/status/2054897826149101588
The article argues that AI agents performing judgment-heavy tasks need feedback loops to improve over time, rather than relying on static prompts, using the example of Buzz, an agent developed by Warp to monitor and respond to social mentions.
Expanding on what we missed with sycophancy
OpenAI provides a deeper technical analysis of the GPT-4o sycophancy issue discovered in April, explaining their post-training and deployment processes, what went wrong with the reward signals, and improvements they're making to evaluation and safety checks.