I ran a quick experiment investigating how DeepSeek-R1 carries out on agentic jobs, despite not supporting tool use natively, and I was rather pleased by initial outcomes. This experiment runs DeepSeek-R1 in a single-agent setup, where the model not just prepares the actions however also creates the actions as executable Python code. On a subset1 of the GAIA recognition split, DeepSeek-R1 outshines Claude 3.5 Sonnet by 12.5% outright, from 53.1% to 65.6% appropriate, and other models by an even larger margin:

The experiment followed design usage guidelines from the DeepSeek-R1 paper and the model card: Don't utilize few-shot examples, avoid adding a system prompt, 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 serve as a representative without being explicitly trained for tool usage. By enabling the model to produce actions as Python code, it can flexibly connect with environments through code execution.

Tools are carried out as Python code that is consisted of straight in the timely. This can be a simple function definition or a module of a bigger package - any valid Python code. The model then generates code actions that call these tools.

Arise from executing these actions feed back to the design as follow-up messages, driving the next steps till a last answer is reached. The agent framework is an easy iterative coding loop that moderates 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 extra context from its environment by utilizing tools e.g. by using a search engine or fetching information from websites. This drives the conversation with the environment that continues till a last answer is reached.

On the other hand, o1 models are understood to carry out inadequately when utilized as chat designs i.e. they don't attempt to pull context during a discussion. According to the linked post, o1 models carry out 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 arise from previous actions included), however this caused significantly lower scores on the GAIA subset. Switching to the conversational method explained above, I had the ability to reach the reported 65.6% performance.

This raises an intriguing concern about the claim that o1 isn't a chat model - maybe this observation was more pertinent to older o1 designs that did not have tool usage abilities? After all, isn't tool use support a crucial system for making it possible for models to pull extra context from their environment? This conversational approach certainly appears efficient for DeepSeek-R1, though I still require to perform comparable 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 tasks with tool use by means of code actions works so well. This capability to generalize to agentic jobs advises of recent research study 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, wiki.rolandradio.net DeepSeek-R1 typically produces long thinking traces at each action, compared to other designs in my experiments, restricting the usefulness of this model in a single-agent setup. Even often take a long period of time to finish. Further RL on agentic tool usage, be it through code actions or not, might be one option to improve effectiveness.

Underthinking

I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a thinking model frequently changes between various thinking ideas without sufficiently checking out promising paths to reach an appropriate option. This was a significant factor for excessively 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 thinking designs is to utilize them for planning just, while using other models for producing code actions. This could be a potential new function of freeact, if this separation of functions proves useful for more complex tasks.

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