I ran a fast experiment examining how DeepSeek-R1 carries out on agentic jobs, regardless of not supporting tool usage natively, and I was rather amazed by preliminary results. This experiment runs DeepSeek-R1 in a single-agent setup, where the model not only prepares the actions but also 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% outright, from 53.1% to 65.6% proper, 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 timely, and set the temperature level to 0.5 - 0.7 (0.6 was utilized). You can discover further evaluation details here.

Approach

DeepSeek-R1's strong coding abilities allow it to function as an agent without being clearly trained for tool use. By enabling the model to produce actions as Python code, it can with environments through code execution.

Tools are executed as Python code that is included straight in the prompt. This can be a simple function definition or drapia.org a module of a bigger bundle - any legitimate Python code. The design then produces code actions that call these tools.

Results from executing these actions feed back to the design as follow-up messages, driving the next actions until a final response is reached. The agent framework is a simple iterative coding loop that moderates the conversation in between the design and its environment.

Conversations

DeepSeek-R1 is used as chat design in my experiment, where the model autonomously pulls extra context from its environment by utilizing tools e.g. by utilizing a search engine or bring data from web pages. This drives the conversation with the environment that continues up until a last answer is reached.

On the other hand, o1 models are known to carry out inadequately when utilized as chat models i.e. they don't try to pull context during a conversation. According to the connected post, o1 designs carry out best when they have the complete context available, with clear guidelines on what to do with it.

Initially, sciencewiki.science I also attempted a complete context in a single timely method at each step (with arise from previous actions consisted of), however this resulted in considerably lower ratings on the GAIA subset. Switching to the conversational method explained above, I was able to reach the reported 65.6% efficiency.

This raises a fascinating concern about the claim that o1 isn't a chat design - perhaps this observation was more relevant to older o1 designs that did not have tool use capabilities? After all, isn't tool usage support an essential mechanism for enabling models to pull extra context from their environment? This conversational technique certainly appears reliable for DeepSeek-R1, though I still require to perform similar try outs o1 models.

Generalization

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

Despite its ability to generalize to tool usage, DeepSeek-R1 often produces extremely long thinking traces at each step, compared to other designs in my experiments, limiting the usefulness of this model in a single-agent setup. Even easier tasks in some cases take a long time to complete. Further RL on agentic tool usage, be it via code actions or not, might be one choice to enhance efficiency.

Underthinking

I also observed the underthinking phenomon with DeepSeek-R1. This is when a reasoning design frequently changes between different thinking ideas without adequately exploring appealing courses to reach an appropriate solution. This was a significant reason for extremely 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 designs is to use them for preparing only, while using other models for producing code actions. This might be a potential new feature of freeact, if this separation of roles proves helpful for more complex tasks.

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