What specific tasks does DeepSeek-R1 struggle with in software engineering

1. Long Evaluation Times: The model struggles with tasks that require extensive verification, which can slow down the reinforcement learning (RL) process. This inefficiency affects its performance in software engineering benchmarks, as the model does not show significant improvements over its predecessor, DeepSeek-V3, in this area[2][3].

2. Sensitivity to Prompting: DeepSeek-R1 is sensitive to the structure and format of prompts. It performs poorly with multi-turn or few-shot prompting scenarios, which are common in software engineering contexts. The recommendation is to use a zero-shot approach for better outcomes, indicating a limitation in its flexibility and adaptability during interactions[2][4].

3. General Capability Limitations: Although DeepSeek-R1 excels in reasoning tasks, it falls short in broader capabilities required for complex software engineering tasks such as function calling and handling JSON outputs. This gap suggests that while it can tackle some coding challenges, it may not be reliable for more intricate programming requirements[3][4].

4. Cultural and Contextual Biases: Training on localized datasets may lead to biases that affect its performance globally. This limitation can hinder its effectiveness in diverse software engineering environments that require a nuanced understanding of various cultural contexts[1][2].

5. Lack of Strong Partnerships: The absence of robust partnerships and integrations with established platforms may limit its adoption among developers who often rely on well-supported tools for software engineering tasks[1][4].

These challenges indicate that while DeepSeek-R1 has made strides in AI capabilities, it still requires further development to fully address the complexities inherent in software engineering tasks.