I ran a quick experiment examining how DeepSeek-R1 performs on agentic tasks, regardless of not supporting tool use natively, and I was rather amazed by preliminary results. This experiment runs DeepSeek-R1 in a single-agent setup, where the model not just plans the actions however likewise creates the actions as executable Python code. On a subset1 of the GAIA recognition split, DeepSeek-R1 outshines Claude 3.5 Sonnet by 12.5% absolute, from 53.1% to 65.6% right, and other models by an even larger margin:
The experiment followed model usage from the DeepSeek-R1 paper and the model card: Don't use few-shot examples, avoid including a system prompt, and set the temperature level to 0.5 - 0.7 (0.6 was used). You can find more evaluation details here.
Approach
DeepSeek-R1's strong coding capabilities enable it to act as a representative without being clearly trained for tool use. By permitting the design to create actions as Python code, it can flexibly communicate with environments through code execution.
Tools are carried out as Python code that is consisted of straight in the timely. This can be a simple function definition or a module of a larger package - any valid Python code. The design then generates code actions that call these tools.
Results from carrying out these actions feed back to the design as follow-up messages, driving the next steps till a last response is reached. The agent framework is a simple iterative coding loop that mediates the conversation in between the model and its environment.
Conversations
DeepSeek-R1 is utilized as chat design in my experiment, where the model autonomously pulls additional context from its environment by using tools e.g. by utilizing an online search engine or fetching information from web pages. This drives the discussion with the environment that continues until a last response is reached.
In contrast, o1 designs are understood to carry out badly when utilized as chat designs i.e. they do not try to pull context throughout a discussion. According to the linked post, o1 designs carry out best when they have the complete context available, with clear guidelines on what to do with it.
Initially, I also attempted a full context in a single prompt method at each step (with arise from previous actions included), however this led to substantially lower scores on the GAIA subset. Switching to the conversational method explained above, I was able to reach the reported 65.6% performance.
This raises an intriguing concern about the claim that o1 isn't a chat design - maybe this observation was more appropriate to older o1 designs that lacked tool use abilities? After all, isn't tool use support an important mechanism for allowing models to pull additional context from their environment? This conversational technique certainly seems effective for DeepSeek-R1, though I still need to carry out similar explores o1 models.
Generalization
Although DeepSeek-R1 was mainly trained with RL on math and coding jobs, it is exceptional that generalization to agentic jobs with tool use through code actions works so well. This ability to generalize to agentic tasks reminds of current research by DeepMind that shows that RL generalizes whereas SFT remembers, although generalization to tool use wasn't examined because work.
Despite its capability to generalize to tool usage, [users.atw.hu](http://users.atw.hu/samp-info-forum/index.php?PHPSESSID=a9e94d89162a17c14e2e1819f530fab0&action=profile
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Exploring DeepSeek R1's Agentic Capabilities Through Code Actions
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