Seven years ago, OpenAI five had just been released, and many people in the AI safety community expected AIs to be opaque RL agents. Luckily, we ended up with reasoning models that speak their thoughts clearly enough for us to follow along (most of the time). In a new multi-org position paper, we argue that we should try to preserve this level of reasoning transparency and turn chain of thought monitorability into a systematic AI safety agenda.
This is a measure that improves safety in the medium term, and it might not scale to superintelligence even if somehow a superintelligent AI still does its reasoning in English. We hope that extending the time when chains of thought are monitorable will help us do more science on capable models, practice more safety techniques "at an easier difficulty", and allow us to extract more useful work from potentially misaligned models.
Abstract: AI systems that "think" in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. Like all other known AI oversight methods, CoT monitoring is imperfect and allows some misbehavior to go unnoticed. Nevertheless, it shows promise and we recommend further research into CoT monitorability and investment in CoT monitoring alongside existing safety methods. Because CoT monitorability may be fragile, we recommend that frontier model developers consider the impact of development decisions on CoT monitorability.
A couple of years ago, the consensus in the safety community was that process-based RL should be prioritized over outcome-based RL [...]
I don't think using outcome-based RL was ever a hard red line, but it was definitely a line of some kind.
I think many people at OpenAI were pretty explicitly keen on avoiding (what you are calling) process-based RL in favor of (what you are calling) outcome-based RL for safety reasons, specifically to avoid putting optimization pressure on the chain of thought. E.g. I argued with @Daniel Kokotajlo about this, I forget whether before or after he left OpenAI.
There were maybe like 5-10 people at GDM who were keen on process-based RL for safety reasons, out of thousands of employees.
I don't know what was happening at Anthropic, though I'd be surprised to learn that this was central to their thinking.
Overall I feel like it's not correct to say that there was a line of some kind, except under really trivial / vacuous interpretations of that. At best it might apply to Anthropic (since it was in the Core Views post).
Separately, I am still personally keen on process-based RL and don't think it's irrelevant -- and indeed we recently published MONA which imo is the most direct experimental paper on the safety idea behind process-based RL.
In general there is a spectrum between process- and outcome-based RL, and I don't think I would have ever said that we shouldn't do outcome-based RL on short-horizon tasks with verifiable rewards; I care much more about the distinction between the two for long-horizon fuzzy tasks.
I do agree that there are some signs that people will continue with outcome-based RL anyway, all the way into long-horizon fuzzy tasks. I don't think this is a settled question -- reasoning models have only been around a year, things can change quite quickly.
None of this is to disagree with your takeaways, I roughly agree with all of them (maybe I'd have some quibbles about #2).
Twitter | Paper PDF
Seven years ago, OpenAI five had just been released, and many people in the AI safety community expected AIs to be opaque RL agents. Luckily, we ended up with reasoning models that speak their thoughts clearly enough for us to follow along (most of the time). In a new multi-org position paper, we argue that we should try to preserve this level of reasoning transparency and turn chain of thought monitorability into a systematic AI safety agenda.
This is a measure that improves safety in the medium term, and it might not scale to superintelligence even if somehow a superintelligent AI still does its reasoning in English. We hope that extending the time when chains of thought are monitorable will help us do more science on capable models, practice more safety techniques "at an easier difficulty", and allow us to extract more useful work from potentially misaligned models.
Abstract: AI systems that "think" in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. Like all other known AI oversight methods, CoT monitoring is imperfect and allows some misbehavior to go unnoticed. Nevertheless, it shows promise and we recommend further research into CoT monitorability and investment in CoT monitoring alongside existing safety methods. Because CoT monitorability may be fragile, we recommend that frontier model developers consider the impact of development decisions on CoT monitorability.