Alignment likely generalizes further than capabilities.
Recently, I was reading this paper which demonstrates how to do online RLHF for alignment of LLMs and a sentence stuck out to me: We conjecture that this is because the reward model (discriminator) usually generalizes better than the policy (generator) This is an offhand remark but it strikes at...
Recently, I was reading this paper which demonstrates how to do online RLHF for alignment of LLMs and a sentence stuck out to me: We conjecture that this is because the reward model (discriminator) usually generalizes better than the policy (generator) This is an offhand remark but it strikes at the core of the LessWrong doom story, as propounded here for which the idea of ‘capabilities generalizing further than alignment’ is central. This is a key point because it is necessary for the self-improvmeent of AI systems, even if initially ‘aligned’ using current methods, to spell doom because even
related reading
- Alignment remains a hard, unsolved problem — LessWronglesswrong.com
- Alignment is not solved but it increasingly looks solvablealigned.substack.com
- Defining alignment research — LessWronglesswrong.com
- Will Capabilities Generalise More? — AI Alignment Forumalignmentforum.org
- Current AIs seem pretty misaligned to me — LessWronglesswrong.com
- The Artificiality of Alignmentjoinreboot.org
- The Universe from an Intentional Stancecasparoesterheld.com
- Thomas Larsen's Shortform — LessWronglesswrong.com
- Models Don't "Get Reward" — LessWronglesswrong.com
- Alignment Is Proven To Be Solvable - by SE Gygesverysane.ai
- How far does alignment midtraining generalize?alignment.openai.com
- Self-Fulfilling Misalignment Data Might Be Poisoning Our AI Modelsturntrout.com