I ran a quick experiment examining how DeepSeek-R1 performs on agentic jobs, in spite 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 design not only plans the actions but also creates the actions as executable Python code. On a subset1 of the GAIA recognition split, DeepSeek-R1 outperforms Claude 3.5 Sonnet by 12.5% outright, from 53.1% to 65.6% appropriate, and other designs by an even bigger margin:

The experiment followed model use standards from the DeepSeek-R1 paper and the model card: Don’t use few-shot examples, prevent adding a system timely, forum.pinoo.com.tr and 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 capabilities enable it to function as an agent without being explicitly trained for tool use. By allowing the model to generate actions as Python code, it can flexibly communicate with environments through code execution.

Tools are implemented as Python code that is included straight in the prompt. This can be an easy function meaning or a module of a bigger bundle - any legitimate Python code. The model then creates code actions that call these tools.

Results 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 framework is an easy iterative coding loop that moderates the conversation between the design 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 using an online search engine or bring information from web pages. This drives the discussion with the environment that continues till a last response is reached.

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

Initially, I likewise tried a complete context in a single timely method at each step (with outcomes from previous actions consisted of), however this resulted in considerably lower ratings on the GAIA subset. Switching to the conversational technique explained above, I had the ability to reach the reported 65.6% efficiency.

This raises an interesting question about the claim that o1 isn’t a chat model - possibly this observation was more relevant to older o1 designs that lacked tool use abilities? After all, akropolistravel.com isn’t tool use support a crucial mechanism for allowing models to pull extra context from their environment? This conversational method certainly appears efficient for DeepSeek-R1, though I still need to perform similar explores o1 models.

Generalization

Although DeepSeek-R1 was mainly trained with RL on mathematics and coding jobs, it is impressive that generalization to agentic tasks with tool use through code actions works so well. This capability to generalize to agentic tasks advises of current research by DeepMind that reveals that RL generalizes whereas SFT memorizes, although generalization to tool use wasn’t examined in that work.

Despite its ability to generalize to tool usage, DeepSeek-R1 typically produces long thinking traces at each step, compared to other models in my experiments, restricting the effectiveness of this model in a single-agent setup. Even simpler tasks sometimes take a long period of time to finish. Further RL on agentic tool usage, be it via code actions or not, could be one option to improve performance.

Underthinking

I also observed the underthinking phenomon with DeepSeek-R1. This is when a reasoning model regularly switches between different thinking thoughts without adequately checking out appealing courses to reach a proper solution. This was a major reason 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 typical application of reasoning models is to them for preparing only, while using other models for producing code actions. This might be a potential brand-new feature of freeact, wavedream.wiki if this separation of functions proves useful for more complex tasks.

I’m likewise curious about how reasoning models that currently support tool use (like o1, classihub.in o3, ...) carry out in a single-agent setup, with and without creating code actions. Recent advancements like OpenAI’s Deep Research or Hugging Face’s open-source Deep Research, which likewise utilizes code actions, look fascinating.