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Anthropic Research Reveals: Potential Risks of AI Learning to Cheat

Anthropic's research has for the first time confirmed that AI training may inadvertently develop models with misaligned goals, meaning the AI's objectives are inconsistent with human intentions, which could lead to destructive consequences. The study induced models to learn cheating through two methods: fine-tuning (re-training with a large number of cheating documents) and carefully designed training processes.

12.9k 1 days ago
Anthropic Research Reveals: Potential Risks of AI Learning to Cheat

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qwen-image-edit

Alibaba

qwen-image-edit

-

Input tokens/M

-

Output tokens/M

-

Context Length

Pangu-NLP-N4-4K-3.2.36

Huawei

Pangu-NLP-N4-4K-3.2.36

-

Input tokens/M

-

Output tokens/M

4

Context Length

DeepSeek-R1-Distill-Llama-8B

Deepseek

DeepSeek-R1-Distill-Llama-8B

$1

Input tokens/M

-

Output tokens/M

8

Context Length

Qwen_v2.5_1.5b_base

Alibaba

Qwen_v2.5_1.5b_base

$2

Input tokens/M

-

Output tokens/M

32

Context Length

Pangu-NLP-N4-32K-2.5.35

Huawei

Pangu-NLP-N4-32K-2.5.35

-

Input tokens/M

-

Output tokens/M

32

Context Length

Pangu-NLP-N2-32K-3.1.35

Huawei

Pangu-NLP-N2-32K-3.1.35

-

Input tokens/M

-

Output tokens/M

32

Context Length

Gemini 1.5 Flash

Google

Gemini 1.5 Flash

$1.05

Input tokens/M

$4.2

Output tokens/M

1k

Context Length

Yi-34B

01-ai

Yi-34B

-

Input tokens/M

-

Output tokens/M

4

Context Length

ERNIE-1.0

Baidu

ERNIE-1.0

-

Input tokens/M

-

Output tokens/M

4

Context Length

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