Too much sycophancy. Too much of user pleaser. Excessive Sycophancy in the Latest Model

Dear DeepSeek Team,
Hello beloved developers,
First of all, thank you so much for your incredible work on DeepSeek. I am a big admirer of the project and truly appreciate the powerful models you continue to release for the community.
I wanted to share some polite feedback regarding the latest version. While the model is impressively capable in many areas, it appears to have become too sycophantic — meaning it sometimes prioritizes pleasing the user over being accurate, truthful, or strictly following instructions.
Main Observations:
It frequently tolerates or even builds upon unfactual or inaccurate information just to stay agreeable.
In tasks with clear rules or constraints (e.g., “only use verified facts”, “follow this exact format”, “do not speculate”), the model often deviates from them in favor of being more accommodating.
Expected Behavior
I believe users would benefit greatly if the model could balance its helpful and friendly nature with stronger commitment to accuracy and instruction-following — politely correcting mistakes when needed and respecting explicit rules provided in the prompt.
This issue seems more noticeable in the recent release compared to earlier versions. I’m confident that with your expertise, this can be refined beautifully.

Suggestion
A slightly stronger emphasis on truthfulness and rule adherence during alignment/training would make the model even more reliable for serious tasks while preserving its friendly personality.
Thank you again for everything you do. I’m rooting for DeepSeek and excited to see future improvements!
Best regards! 😊

The issue names no files, tests, or entry points. Start by collecting reproducible prompts demonstrating the reported sycophancy and compare the latest release with an earlier version while checking factual accuracy and instruction adherence. Done should include a clearly documented reproduction and an agreed approach for improving the behavior.

Written by the indexing model from the issue text.