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I ran a fast experiment examining how DeepSeek-R1 performs on agentic tasks, in spite of not supporting tool usage natively, and I was quite amazed by preliminary results. This experiment runs DeepSeek-R1 in a single-agent setup, where the design not just plans the actions however also formulates the actions as executable Python code. On a subset1 of the GAIA validation split, DeepSeek-R1 outperforms 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 use guidelines from the DeepSeek-R1 paper and the model card: Don't utilize few-shot examples, prevent adding a system timely, and koha-community.cz set the temperature level to 0.5 - 0.7 (0.6 was utilized). You can find additional 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 allowing 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 included straight in the prompt. This can be an easy function definition or asystechnik.com a module of a bigger package - any valid Python code. The model then creates code actions that call these tools.

Results from executing these actions feed back to the design as follow-up messages, driving the next actions till a final answer is reached. The agent framework is an easy iterative coding loop that moderates the discussion between the model and its environment.

Conversations

DeepSeek-R1 is used as chat design 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 fetching data from web pages. This drives the conversation with the environment that continues up until a final response is reached.

In contrast, o1 designs are known to carry out improperly when used as chat models i.e. they don't try to pull context throughout a conversation. According to the linked article, o1 models perform best when they have the complete context available, with clear guidelines on what to do with it.

Initially, I also attempted a complete context in a single prompt technique at each action (with results from previous steps consisted of), but this resulted in significantly lower ratings on the GAIA subset. Switching to the conversational approach explained above, I was able to reach the reported 65.6% efficiency.

This raises a fascinating question about the claim that o1 isn't a chat model - possibly this observation was more relevant to older o1 models that lacked tool use capabilities? After all, isn't tool use support an essential mechanism for enabling designs to pull additional context from their environment? This conversational technique certainly appears efficient for DeepSeek-R1, though I still need to perform similar try outs o1 designs.

Generalization

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

Despite its ability to generalize to tool use, DeepSeek-R1 frequently produces very long reasoning traces at each step, compared to other models in my experiments, limiting the effectiveness of this design in a single-agent setup. Even simpler tasks often take a very long time to finish. Further RL on agentic tool use, be it by means of code actions or not, larsaluarna.se might be one choice to improve efficiency.

Underthinking

I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a thinking model regularly switches in between various reasoning thoughts without sufficiently checking out appealing paths to reach a proper service. This was a significant 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 preparing just, while using other models for creating code actions. This could be a prospective new feature of freeact, if this separation of functions shows helpful for more complex jobs.

I'm likewise curious about how thinking designs that currently support tool use (like o1, library.kemu.ac.ke o3, ...) carry out in a setup, with and without producing code actions. Recent advancements like OpenAI's Deep Research or Hugging Face's open-source Deep Research, which likewise uses code actions, look interesting.