Exploring DeepSeek-R1’s Agentic Capabilities Through Code Actions

I ran a fast experiment examining how DeepSeek-R1 performs on agentic tasks, regardless of not supporting tool usage natively, and fakenews.win I was quite impressed by preliminary results. This experiment runs DeepSeek-R1 in a single-agent setup, where the design not only prepares the actions however likewise develops 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% correct, and other designs by an even larger margin:

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

Approach

DeepSeek-R1’s strong coding abilities enable it to act as an agent without being clearly trained for tool usage. By enabling 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 consisted of straight in the prompt. This can be an easy function meaning or a module of a bigger package – any legitimate Python code. The model then generates code actions that call these tools.

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

Conversations

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

In contrast, o1 models are known to carry out poorly when utilized as chat designs i.e. they don’t to pull context throughout a discussion. According to the connected short article, o1 designs perform best when they have the full context available, with clear guidelines on what to do with it.

Initially, I also tried a complete context in a single prompt method at each step (with outcomes from previous actions consisted of), but this led to substantially lower scores on the GAIA subset. Switching to the conversational approach explained above, I was able to reach the reported 65.6% performance.

This raises a fascinating concern about the claim that o1 isn’t a chat design – maybe this observation was more pertinent to older o1 models that did not have tool usage abilities? After all, isn’t tool use support a crucial system for making it possible for models to pull extra context from their environment? This conversational approach certainly seems effective for DeepSeek-R1, though I still require to conduct comparable explores o1 designs.

Generalization

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

Despite its capability to generalize to tool usage, DeepSeek-R1 frequently produces extremely long reasoning traces at each action, compared to other models in my experiments, limiting the usefulness of this design in a single-agent setup. Even easier jobs sometimes take a long time to complete. Further RL on agentic tool use, be it by means of code actions or not, might be one alternative to enhance effectiveness.

Underthinking

I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a reasoning model often changes in between different reasoning thoughts without sufficiently exploring appealing courses to reach a correct option. This was a significant factor users.atw.hu for overly long reasoning traces produced by DeepSeek-R1. This can be seen in the recorded traces that are available for download.

Future experiments

Another common application of reasoning models is to utilize them for planning just, while using other designs for producing code actions. This might be a potential brand-new function of freeact, if this separation of roles proves helpful for more complex jobs.

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