clevr_cogen_a_train数据集上，think的结果和answer的结果不一致

[xjx0524]

Content: <think>
The image contains a few distinct objects:

1. A large purple cylinder.
2. A small purple cylinder.
3. A matte yellow cube.
4. A small gray metallic sphere.

All of these objects appear to be separate and distinct without overlap or attachment.
</think>
<answer>
5
</answer>
Solution: <answer> 5 </answer>

按实例代码跑的GRPO，目前训练到20%，发现很多都是think数错了，但是answer对了，这个怎么理解呢？

[Racktic]

Same observation! Seems that the model just surprisingly guess the correct answer.

[austingg]

There are no supervised signal for content, only format_reward and accuracy_reward, if you look into DeepSeek R1 's CoT, there are also something wrong when you ask math problem

[m-Just]

Because the pre-trained model is already pretty good at directly answering such questions without CoT. See #72.

@austingg That's interesting. Do you mind sharing some DeepSeek R1 wrong CoT examples (but with correct answers) here? Or could you point me to the place where I can find such examples?