I ran a quick experiment investigating how DeepSeek-R1 carries out on agentic tasks, regardless of not supporting tool usage natively, and I was quite pleased by preliminary outcomes. This experiment runs DeepSeek-R1 in a single-agent setup, where the design not just plans the actions but also formulates the actions as executable Python code. On a subset1 of the GAIA validation split, DeepSeek-R1 outperforms Claude 3.5 Sonnet by 12.5% absolute, from 53.1% to 65.6% correct, and other models by an even bigger margin:
The experiment followed model use standards from the DeepSeek-R1 paper and the design card: Don't use few-shot examples, surgiteams.com prevent including a system prompt, bbarlock.com and set the temperature to 0.5 - 0.7 (0.6 was used). You can find additional examination details here.
Approach
DeepSeek-R1's strong coding capabilities enable it to serve as an agent without being clearly trained for tool usage. By permitting the design to generate actions as Python code, it can flexibly connect with environments through code execution.
Tools are implemented as Python code that is consisted of straight in the timely. This can be an easy function meaning or a module of a bigger plan - any valid Python code. The model then produces code actions that call these tools.
Results from performing these actions feed back to the design as follow-up messages, driving the next actions up until a final response is reached. The agent framework is a basic iterative coding loop that mediates the conversation in between the model and its environment.
Conversations
DeepSeek-R1 is used as chat design in my experiment, where the model autonomously pulls extra context from its environment by utilizing tools e.g. by utilizing a search engine or fetching information from websites. This drives the conversation with the environment that continues till a final answer is reached.
In contrast, [users.atw.hu](http://users.atw.hu/samp-info-forum/index.php?PHPSESSID=859cccb730b1df76362d3182e962062f&action=profile
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Exploring DeepSeek R1's Agentic Capabilities Through Code Actions
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