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 initial results. This experiment runs DeepSeek-R1 in a single-agent setup, where the design not just plans the actions but also creates the actions as executable Python code. On a subset1 of the GAIA recognition split, DeepSeek-R1 surpasses Claude 3.5 Sonnet by 12.5% outright, from 53.1% to 65.6% proper, and other models by an even larger margin:

The experiment followed model use guidelines from the DeepSeek-R1 paper and the design card: Don’t use few-shot examples, prevent adding a system prompt, and set the temperature level to 0.5 - 0.7 (0.6 was used). You can find further evaluation details here.

Approach

DeepSeek-R1’s strong coding abilities allow it to act as a representative without being explicitly trained for tool usage. By permitting the model to generate actions as Python code, it can flexibly interact 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 meaning or a module of a bigger package - any valid Python code. The design then generates code actions that call these tools.

Arise from carrying out these actions feed back to the model as follow-up messages, driving the next steps until a final response is reached. The representative framework is a simple iterative coding loop that mediates the discussion between the model and its environment.

Conversations

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

In contrast, wiki.rolandradio.net o1 models are understood to perform improperly when utilized as chat models i.e. they do not attempt to pull context during a conversation. According to the linked article, lespoetesbizarres.free.fr o1 designs carry out best when they have the complete context available, with clear guidelines on what to do with it.

Initially, visualchemy.gallery I also tried a full context in a single timely method at each action (with arise from previous steps included), however this caused substantially lower ratings on the GAIA subset. Switching to the conversational approach explained above, I was able to reach the reported 65.6% performance.

This raises an interesting question about the claim that o1 isn’t a chat design - possibly this observation was more appropriate to older o1 models that did not have tool usage abilities? After all, isn’t tool usage support a crucial mechanism for allowing designs to pull extra context from their environment? This conversational approach certainly appears reliable for DeepSeek-R1, though I still need to perform comparable try outs o1 designs.

Generalization

Although DeepSeek-R1 was mainly trained with RL on mathematics and coding jobs, it is impressive that generalization to agentic tasks with tool usage via code actions works so well. This capability to generalize to agentic jobs reminds of recent research by DeepMind that reveals that RL generalizes whereas SFT remembers, although generalization to tool use wasn’t examined in that work.

Despite its capability to generalize to tool use, DeepSeek-R1 typically produces long thinking traces at each step, compared to other designs in my experiments, limiting the effectiveness of this model in a single-agent setup. Even simpler tasks in some cases take a long time to finish. Further RL on agentic tool use, be it via code actions or not, could be one option to enhance effectiveness.

Underthinking

I also observed the underthinking phenomon with DeepSeek-R1. This is when a reasoning design regularly changes between different thinking thoughts without adequately exploring promising paths to reach an appropriate solution. This was a major factor for extremely long reasoning traces produced by DeepSeek-R1. This can be seen in the taped traces that are available for download.

Future experiments

Another common application of reasoning models is to use them for preparing only, visualchemy.gallery while utilizing other designs for creating code actions. This could be a potential new function of freeact, if this separation of roles shows helpful for more complex jobs.

I’m also curious about how thinking designs that currently support tool use (like o1, o3, …) carry out in a single-agent setup, with and without creating code actions. Recent developments like OpenAI’s Deep Research or Hugging Deep Research, which likewise uses code actions, look intriguing.