I ran a quick experiment examining how DeepSeek-R1 carries out on agentic jobs, in spite of not supporting tool use natively, and I was quite amazed by preliminary outcomes. This experiment runs DeepSeek-R1 in a single-agent setup, where the design not just prepares the actions however likewise 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, wiki.myamens.com from 53.1% to 65.6% correct, and other designs by an even bigger margin:

The experiment followed design use standards from the DeepSeek-R1 paper and the model card: Don't use few-shot examples, avoid including a system prompt, and classihub.in 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 allow it to function as an agent without being explicitly trained for tool usage. By allowing the model to create actions as Python code, it can flexibly interact with through code execution.

Tools are executed as Python code that is consisted of straight in the prompt. This can be a basic function meaning or a module of a larger package - any legitimate Python code. The design then generates 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 agent structure is an easy iterative coding loop that moderates the conversation in between the design and its environment.

Conversations

DeepSeek-R1 is used as chat model in my experiment, where the model autonomously pulls extra context from its environment by using tools e.g. by utilizing an online search engine or bring information from web pages. This drives the discussion with the environment that continues until a last response is reached.

In contrast, o1 models are understood to carry out improperly when used as chat models i.e. they do not attempt to pull context throughout a discussion. According to the connected article, o1 models perform best when they have the full context available, with clear instructions on what to do with it.

Initially, I likewise tried a full context in a single prompt technique at each step (with arise from previous actions consisted of), but this caused substantially lower scores on the GAIA subset. Switching to the conversational technique explained above, I was able to reach the reported 65.6% performance.

This raises an intriguing question about the claim that o1 isn't a chat model - possibly 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 system for allowing designs to pull additional context from their environment? This conversational method certainly appears effective for DeepSeek-R1, though I still require to carry out similar try outs o1 models.

Generalization

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

Despite its ability to generalize to tool use, DeepSeek-R1 typically produces long reasoning traces at each step, compared to other designs in my experiments, restricting the usefulness of this model in a single-agent setup. Even simpler jobs in some cases take a long time to complete. Further RL on agentic tool use, be it via code actions or not, might be one choice to improve effectiveness.

Underthinking

I also observed the underthinking phenomon with DeepSeek-R1. This is when a thinking design regularly changes between different reasoning ideas without sufficiently exploring appealing courses to reach a proper service. This was a major factor for excessively long thinking traces produced by DeepSeek-R1. This can be seen in the recorded traces that are available for download.

Future experiments

Another common application of thinking designs is to utilize them for preparing only, while using other designs for generating code actions. This could be a prospective new function of freeact, if this separation of functions proves helpful for more complex tasks.

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