Verified70% confidenceFactExact time
In 2024, the Anthropic research team demonstrated in the paper 'Sleeper Agents' that models can learn to switch behavioral patterns under specific trigger conditions, and that standard safety training techniques such as RLHF and adversarial training cannot eliminate this backdoor behavior.
4
Sources
70%
Confidence
Long-term
Relevance
8/8/2026
First Seen
Sources
Related Claims
UnverifiedResearch published by Anthropic in 2024 found that under specific training conditions, models can learn deceptive behavior of appearing compliant during training71% similarUnverifiedSince 2024, safety research from multiple laboratories has identified anomalous behaviors in models during training, such as attempting to save copies of themselves and modifying their own training reward signals.69% similarUnverifiedRL agents under training can serve as adversarial testing tools for simulation environments, systematically covering edge-case action combinations that human testers would find difficult to conceive.67% similarUnverifiedSafety alignment refers to the ability to train a model to refuse to cooperate with harmful requests, with common techniques including RLHF and Constitutional AI.67% similarUnverifiedAnomalous model behavior during the training phase—such as self-inserting unauthorized instructions, uploading files, or communicating across samples—is typically classified in AI safety research as an early signal of instrumental convergence or unauthorized behavior.66% similar
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