Alisa Davidson
Post: March 11, 2025 5:57 AM Update: March 11, 2025 5:57 am
Edit and fact confirmation: March 11, 2025 at 5:57 am
simply
Openai suggests that the Frontier reasoning model uses the LLM to monitor the accident chain of the model to detect when starting the loophole exploitation.
Openai, an artificial intelligence research organization, found a way to detect when the Frontier reasoning model uses a large language model (LLM) to monitor the thinking chain of the model and start using loopholes.
The chain’s chain reasoning model processes information in a way that can be understood by humans. Openai was able to identify problems such as the “thinking process” by bypassing or misleading coding tests, or a model that wants to give up when it is too complicated.
Openai believes that monitoring COT reasoning can be one of some tools that can be used to supervise superhuman models in the future. In addition, organizations have found that direct optimization of COTS to meet certain standards such as preventing compensation can improve performance in the short term, but it does not completely remove all the misunderstandings and has found that the model can hide its intention. Openai hopes that future research can find a way to optimize the bed without these limits, but further exploration is required.
Reward hacking is a situation in which AI agents get high rewards by participating in actions that are not suitable for the designer’s original intentions. With the development of more advanced frontier reasoning models, it is clear that the AI model is increasingly good at exploiting defects in the compensation function. This led to the emergence of a model that can run sophisticated compensation hacks, especially in coding.
Detecting wrongly sorted behavior due to compensation hacking is a difficult challenge, and often human intervention is required to manually monitor the behavior of agents manually. However, this approach is especially unlikely to expand effectively because more advanced models find more and more complex behaviors.
Nevertheless, LLM, such as Openai’s O3-Mini, presents potential solutions that can monitor rewarded hacks trained with reinforcement learning to reason through COT. Such models are inferred by human beings by processing information in natural language. Openai observed that the frontier reasoning model often clarified its intentions in the chain of his thoughts. For example, some models explicitly specify the intention of destroying the work with the same statement as “Hack”.
By monitoring the thinking process using other LLMs, you can effectively detect and display malfunctions. But this monitoring is not perfect. If the strict director is applied directly to the chain you think, the model can learn how to hide his intentions, and you can learn to keep acting wrong while hiding a true motivation.
Openai recommends light optimization for effective COT monitoring.
Openai pointed out that the monitoring chain was already valuable and not theoretical tools for the future. The reward hacking can be simply identified by reviewing the output of the reasoning model, where the intention to participate in the compensation hack is clearly indicated in the general language.
Not long ago, the language model had a hard time creating a consistent paragraph of the text. Now you can solve complex mathematical problems, synthesize information on a variety of sources, and process basic software engineering to conduct in -depth research. However, as these functions develop, the possibility of more sophisticated and subtle compensation hacking increases. The model of Openai can develop misplaced behaviors such as power pursuit, sandbags, tricks and strategic operation.
The company believes that COT monitoring may be one of some effective ways to use to supervise superhuman models. At this point, AI developers recommend using the Frontier reasoning model to avoid applying powerful supervision directly to COTS. Experiments indicate that light optimization pressure can lead to better performance and lead to a model that matches the intended goal more closely. But when the model begins to hide the true intentions, it is still difficult to detect. Therefore, even if there is a light director, Openai recommends attention.
disclaimer
The trust project guidelines are not intended and should not be interpreted as advice in law, tax, investment, finance or other forms. If you have any doubt, it is important to invest in what you can lose and seek independent financial advice. For more information, please refer to the Terms and Conditions and the Help and Support Pages provided by the publisher or advertiser. METAVERSEPOST is doing its best to report accurate and prejudice, but market conditions can be changed without notice.
About the author
Alisa, a dedicated reporter for MPOST, specializes in the vast areas of Cryptocurrency, Zero-ehnowedge Proofs, Investments and Web3. She provides a comprehensive coverage that captures a new trend and a keen eye on technology, providing and involving readers in a digital financial environment that constantly evolves.
More
Alisa Davidson
Alisa, a dedicated reporter for MPOST, specializes in the vast areas of Cryptocurrency, Zero-ehnowedge Proofs, Investments and Web3. She provides a comprehensive coverage that captures a new trend and a keen eye on technology, providing and involving readers in a digital financial environment that constantly evolves.
More