I ran a fast experiment investigating how DeepSeek-R1 carries out on agentic tasks, in spite 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 only plans the actions however likewise creates the actions as executable Python code. On a subset1 of the GAIA validation split, DeepSeek-R1 outshines Claude 3.5 Sonnet by 12.5% absolute, from 53.1% to 65.6% appropriate, and other designs by an even larger margin:

The experiment followed design use guidelines from the DeepSeek-R1 paper and the design card: Don't utilize few-shot examples, avoid including a system timely, and set the temperature to 0.5 - 0.7 (0.6 was utilized). You can discover more evaluation details here.

Approach

DeepSeek-R1's strong coding abilities enable it to serve as an agent without being clearly trained for tool usage. By enabling the model 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 an easy function definition or wiki.vst.hs-furtwangen.de a module of a bigger package - any valid Python code. The model then produces code actions that call these tools.

Arise from performing these actions feed back to the model as follow-up messages, driving the next actions up until a last response is reached. The representative structure is a simple iterative coding loop that moderates the conversation in between the model and its environment.

Conversations

DeepSeek-R1 is utilized as chat design in my experiment, where the design autonomously pulls extra context from its environment by utilizing tools e.g. by using an online search engine or fetching information from web pages. This drives the conversation with the environment that continues till a last answer is reached.

In contrast, o1 designs are understood to carry out poorly when used as chat models i.e. they don't attempt to pull context throughout a conversation. According to the connected post, o1 models carry out best when they have the complete context available, addsub.wiki with clear directions on what to do with it.

Initially, I also tried a complete context in a single timely method at each step (with arise from previous steps consisted of), however this led to substantially lower scores on the GAIA subset. Switching to the conversational technique explained above, I was able to reach the reported 65.6% efficiency.

This raises an intriguing question about the claim that o1 isn't a chat design - maybe this observation was more appropriate to older o1 models that did not have tool use capabilities? After all, isn't tool usage support an important mechanism for making it possible for designs to pull extra context from their environment? This conversational technique certainly seems reliable for DeepSeek-R1, though I still require to carry out similar experiments with o1 models.

Generalization

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

Despite its capability to generalize to tool use, DeepSeek-R1 often produces long reasoning traces at each action, compared to other designs in my experiments, restricting the usefulness of this model in a single-agent setup. Even easier jobs often 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 to improve effectiveness.

Underthinking

I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a thinking design regularly switches in between different thinking thoughts without adequately checking out promising courses to reach a correct option. This was a significant reason for overly long thinking traces produced by DeepSeek-R1. This can be seen in the tape-recorded traces that are available for download.

Future experiments

Another typical application of thinking models is to use them for planning only, while using other designs for creating code actions. This might be a possible brand-new feature of freeact, if this separation of roles proves useful for more complex jobs.

I'm also curious about how reasoning models that currently support tool use (like o1, o3, ...) carry out 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 also uses code actions, look fascinating.