I ran a quick experiment investigating how DeepSeek-R1 performs on agentic tasks, regardless of not supporting tool usage natively, and I was rather pleased by initial results. This experiment runs DeepSeek-R1 in a single-agent setup, where the design not only prepares the actions but also 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% appropriate, and other models by an even bigger margin:

The experiment followed design use standards from the DeepSeek-R1 paper and the design card: wiki.snooze-hotelsoftware.de Don't utilize few-shot examples, prevent adding a system timely, and set the temperature level to 0.5 - 0.7 (0.6 was used). You can discover additional evaluation details here.

Approach

DeepSeek-R1's strong coding capabilities enable it to serve as an agent without being explicitly trained for tool use. By allowing the design to create actions as Python code, it can flexibly engage with environments through code execution.

Tools are carried out as Python code that is included straight in the timely. This can be an easy function meaning or a module of a bigger package - any valid Python code. The model 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 actions up until a last response is reached. The agent structure is a simple iterative coding loop that mediates the conversation in between the model and its environment.

Conversations

DeepSeek-R1 is utilized as chat model in my experiment, where the design autonomously pulls extra context from its environment by utilizing tools e.g. by utilizing an online search engine or bring data from web pages. This drives the discussion with the environment that continues up until a last answer is reached.

In contrast, library.kemu.ac.ke o1 designs are known to carry out inadequately when utilized as chat designs i.e. they don't attempt to pull context throughout a conversation. According to the connected post, o1 designs carry out best when they have the complete context available, with clear directions on what to do with it.

Initially, I also tried a complete context in a single prompt method at each action (with arise from previous actions included), however this led to considerably 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 about the claim that o1 isn't a chat model - possibly this observation was more appropriate to older o1 models that did not have tool use capabilities? After all, isn't tool use support an essential system for making it possible for designs to pull extra context from their environment? This conversational approach certainly appears reliable for setiathome.berkeley.edu DeepSeek-R1, though I still need to carry out similar explores o1 models.

Generalization

Although DeepSeek-R1 was mainly trained with RL on mathematics and coding tasks, it is remarkable that generalization to agentic jobs with tool usage through code actions works so well. This ability to generalize to agentic jobs reminds of recent research study by DeepMind that reveals that RL generalizes whereas SFT memorizes, although generalization to tool usage wasn't investigated because work.

Despite its capability to generalize to tool usage, DeepSeek-R1 frequently produces long reasoning traces at each action, compared to other designs in my experiments, limiting the usefulness of this design in a single-agent setup. Even easier tasks in some cases take a very long time to finish. Further RL on agentic tool use, be it by means of code actions or not, might be one alternative to improve efficiency.

Underthinking

I also observed the underthinking phenomon with DeepSeek-R1. This is when a thinking model often switches between various reasoning ideas without adequately checking out promising courses to reach an appropriate service. This was a significant factor for excessively long thinking traces produced by DeepSeek-R1. This can be seen in the tape-recorded traces that are available for download.

Future experiments

Another common application of reasoning models is to use them for planning just, while utilizing other designs for producing code actions. This could be a possible brand-new function of freeact, if this separation of roles proves beneficial for more complex tasks.

I'm also curious about how reasoning models that already support tool use (like o1, o3, ...) perform in a single-agent setup, with and opensourcebridge.science without creating code actions. Recent advancements like OpenAI's Deep Research or Hugging Face's open-source Deep Research, which likewise uses code actions, look intriguing.