OpenAI researchers say training AI with process supervision, which rewards the thought process rather than the outcome, could help prevent hallucinations
- AI hallucinations occur when models like OpenAI's ChatGPT or Google's Bard fabricate information entirely.
CNBCHayden Field
Context & Ripple Effects
The report follows expert accounts of confabulation as models filling gaps in their training data with plausible language, framing hallucinations as a training-and-evaluation problem rather than merely a user-interface flaw. Earlier analysis of how ChatGPT fills informational gaps provides the mechanism this proposal targets.
It also anticipates a later OpenAI argument that conventional evaluation can reward guessing instead of uncertainty. The later critique of guess-rewarding model evaluation makes process supervision relevant as an alternative incentive design, not simply a filter for bad outputs.
First-order effects
OpenAI researchers shift attention toward training signals that assess intermediate reasoning, rather than rewarding only a correct-looking final answer.
For model builders, hallucination reduction becomes partly a supervision-design task: systems must distinguish sound reasoning and appropriate uncertainty from confident fabrication.
Second-order effects
Developers competing on reliability face pressure to build evaluation methods that test reasoning quality and uncertainty, not just answer-level accuracy; benchmark claims based solely on outputs become less complete.
Enterprise adopters gain a clearer basis for demanding assurance evidence around how a model reaches answers, particularly in workflows where unsupported claims are costly.
Third-order effects
If process-based evaluation proves scalable, AI reliability could move from post-deployment guardrails toward training-time assurance, making supervision data and evaluation design more important competitive inputs.
The broader constraint remains that more visible reasoning does not by itself establish truth; durable AI governance will likely require combining training incentives with independent testing and workflow controls.
The trend: This is an early signal of operational AI assurance shifting from judging model answers alone to shaping—and auditing—the incentives behind them.
We trained an AI using process supervision — rewarding the thought process rather than the outcome — to achieve new state-of-art in mathematical reasoning. Encouraging sign for alignment of advanced AIs: ...https://openai.com/...
Really interesting result on using LLMs to do math: Supervising every step works better than only checking the answer. Some thoughts how this matters for alignment 👇 https://openai.com/...
Initial results with process supervision — training an AI by rewarding it for coming up with a correct thought process, not just outputting the right answer. Great results in mathematical reasoning: https://twitter.com/...
“Rewarding the thought process” I believe will impact other domains. Not to mention it rings philosophical. Philosophy + Math The “thought process rewarded” is a stepping stone on the pathway to “AI Awareness”. I say “AI Awareness because unlike “Human Awareness” the AI... https:…
[This is a big deal.] New @OpenAI model sets a new standard in math problem-solving by rewarding each correct reasoning step, not just the final answer. This improves performance and aligns the AI's reasoning with human-endorsed thought processes. https://cdn.openai.com/...
Next step is RLHF with the process supervision RM. They more than likely tried it and the fact that it is not reported, prolly means that it works well 🙃 But with the release of the dataset we can try it for ourselves on Llama or Falcon 🔥 https://twitter.com/...
.@Microsoft-backed @OpenAI has released a new research paper with ideas on how to help prevent the #AI #hallucinations. @theCUBE @EvanKirstel @BillMew https://www.cnbc.com/...
Earlier I tweeted that intervention is critical for solving robotics. OpenAI just showed intervention(process supervision) improves math skills 😇 I'd bet that many problems unsolvable by LLMs today will ease with intervention and give interpretability of reasoning for free https:…
i'm somewhat inclined to believe this is falsy regulatory cope but very interesting if true on a pure capabilities basis https://twitter.com/... [image]
OpenAI is taking up the mantle against AI “hallucinations” or falsehoods, it announced Wednesday, with a newer AI training method. The research comes at a time when misinfo from AI systems is more hotly debated than ever. Experts expressed their skepticism.https://www.cnbc.com/ .…
“Process Supervision” vs. “Outcome Supervision” -> OpenAI is pursuing a new way to fight A.I. ‘hallucinations’ “Train AI models to reward themselves for each individual correct step of reasoning when they're arriving at an answer.” https://www.cnbc.com/... [image]