I ran a quick experiment examining how DeepSeek-R1 carries out on agentic tasks, in spite of not supporting tool usage natively, and I was rather impressed by preliminary outcomes. 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 validation split, DeepSeek-R1 outshines Claude 3.5 Sonnet by 12.5% outright, from 53.1% to 65.6% right, and other designs 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 including a system timely, 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 allow it to function as a representative without being explicitly trained for tool usage. By permitting the design to create actions as Python code, it can flexibly interact with environments through code execution.

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

Arise from executing these actions feed back to the model as follow-up messages, grandtribunal.org driving the next steps up until a final response is reached. The agent framework is a simple iterative coding loop that mediates the discussion in between the model and its environment.

Conversations

DeepSeek-R1 is utilized as chat model in my experiment, where the model autonomously pulls additional context from its environment by utilizing tools e.g. by using a 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 understood to perform improperly when utilized as chat models i.e. they do not attempt to pull context throughout a discussion. According to the connected short article, o1 designs carry out best when they have the complete context available, with clear directions on what to do with it.

Initially, I likewise attempted a complete context in a single timely approach at each step (with results from previous actions included), but this resulted in scores on the GAIA subset. Switching to the conversational method explained above, I was able to reach the reported 65.6% efficiency.

This raises an interesting question about the claim that o1 isn't a chat design - possibly this observation was more pertinent to older o1 models that lacked tool usage capabilities? After all, trademarketclassifieds.com isn't tool use support a crucial system for enabling designs to pull additional context from their environment? This conversational technique certainly seems efficient for DeepSeek-R1, though I still require to perform similar experiments with o1 models.

Generalization

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

Despite its ability to generalize to tool usage, DeepSeek-R1 frequently produces long reasoning traces at each step, compared to other designs in my experiments, restricting the effectiveness of this model in a single-agent setup. Even easier jobs often take a long time to finish. Further RL on agentic tool use, be it by means of code actions or not, could be one option to enhance efficiency.

Underthinking

I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a reasoning design frequently changes between different thinking thoughts without adequately checking out appealing courses to reach a right option. 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 models is to utilize them for preparing only, while utilizing other models for producing code actions. This might be a possible new feature of freeact, if this separation of functions shows beneficial for more complex jobs.

I'm also curious about how thinking designs that currently support tool usage (like o1, o3, ...) perform 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 uses code actions, look intriguing.