I ran a quick experiment examining how DeepSeek-R1 performs on agentic jobs, in spite of not supporting tool usage natively, and I was rather satisfied by preliminary outcomes. This experiment runs DeepSeek-R1 in a single-agent setup, where the model not just prepares the actions but likewise develops the actions as executable Python code. On a subset1 of the GAIA validation split, DeepSeek-R1 exceeds 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 guidelines from the DeepSeek-R1 paper and the model card: Don't use few-shot examples, prevent including a system timely, koha-community.cz and historydb.date set the temperature level to 0.5 - 0.7 (0.6 was utilized). You can discover more examination details here.

Approach

DeepSeek-R1's strong coding abilities enable it to function as a representative without being explicitly trained for tool use. By permitting the design to create actions as Python code, it can flexibly interact with environments through code execution.

Tools are carried out as Python code that is included straight in the prompt. This can be a basic function meaning or a module of a larger package - any valid Python code. The design 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 until a last response is reached. The representative structure is an easy iterative coding loop that moderates the discussion in between the design and its environment.

Conversations

DeepSeek-R1 is utilized as chat design in my experiment, morphomics.science 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 conversation with the environment that continues up until a final response is reached.

In contrast, o1 models are understood to perform improperly when utilized as chat designs i.e. they don't attempt to pull context throughout a discussion. According to the linked article, classicrock.awardspace.biz o1 models perform best when they have the complete context available, with clear directions on what to do with it.

Initially, I also attempted a complete context in a single timely approach at each step (with outcomes from previous actions consisted of), however this caused considerably lower ratings on the GAIA subset. Switching to the conversational technique explained above, wino.org.pl I was able to reach the reported 65.6% performance.

This raises an interesting question about the claim that o1 isn't a chat model - perhaps this observation was more relevant to older o1 designs that lacked tool usage capabilities? After all, isn't tool use support an essential system for making it possible for models to pull extra context from their environment? This conversational method certainly seems efficient for DeepSeek-R1, though I still require to conduct similar experiments with o1 models.

Generalization

Although DeepSeek-R1 was mainly trained with RL on mathematics and coding tasks, it is exceptional that generalization to agentic jobs with tool use by means of code actions works so well. This capability to generalize to agentic jobs advises of recent research by DeepMind that reveals that RL generalizes whereas SFT memorizes, although generalization to tool use wasn't investigated in that work.

Despite its ability to generalize to tool usage, DeepSeek-R1 often produces long thinking traces at each action, compared to other designs in my experiments, restricting the usefulness of this design in a single-agent setup. Even easier jobs sometimes take a long time to finish. Further RL on agentic tool usage, be it via code actions or not, could be one choice to improve performance.

Underthinking

I also observed the underthinking phenomon with DeepSeek-R1. This is when a reasoning design regularly changes in between various thinking thoughts without adequately checking out appealing courses to reach a correct service. This was a major reason for excessively 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 thinking designs is to use them for planning only, while utilizing other designs for humanlove.stream creating code actions. This might be a potential brand-new feature of freeact, if this separation of roles proves helpful for more complex tasks.

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