I ran a fast experiment examining how DeepSeek-R1 carries out on agentic tasks, despite not supporting tool usage natively, and I was quite impressed by preliminary outcomes. This experiment runs DeepSeek-R1 in a single-agent setup, townshipmarket.co.za where the design not just plans the actions but likewise creates the actions as executable Python code. On a subset1 of the GAIA validation split, DeepSeek-R1 surpasses Claude 3.5 Sonnet by 12.5% outright, from 53.1% to 65.6% proper, and other models by an even larger margin:

The experiment followed model usage guidelines from the DeepSeek-R1 paper and the model card: Don’t utilize few-shot examples, prevent adding a system timely, and set the temperature to 0.5 - 0.7 (0.6 was used). You can find more evaluation details here.

Approach

DeepSeek-R1‘s strong coding abilities allow it to serve as an agent without being clearly trained for tool usage. By allowing the model to produce actions as Python code, it can flexibly interact with environments through code execution.

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

Results from executing these actions feed back to the model as follow-up messages, driving the next actions till a last response is reached. The agent structure is a basic iterative coding loop that mediates the discussion in between the design and wiki.vifm.info its environment.

Conversations

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

On the other hand, o1 designs are understood to carry out badly when used as chat models i.e. they don’t attempt to pull context throughout a conversation. According to the connected short article, o1 designs perform best when they have the complete context available, with clear directions on what to do with it.

Initially, I likewise tried a complete context in a single prompt approach at each step (with outcomes from previous steps included), but this led to considerably lower ratings on the GAIA subset. Switching to the conversational technique explained above, I had the ability to reach the reported 65.6% performance.

This raises an intriguing question about the claim that o1 isn’t a chat design - perhaps this observation was more pertinent to older o1 designs that did not have tool usage capabilities? After all, isn’t tool use support an important system for enabling designs to pull additional context from their environment? This conversational technique certainly appears effective for DeepSeek-R1, though I still require to carry out comparable try outs o1 models.

Generalization

Although DeepSeek-R1 was mainly trained with RL on mathematics and yewiki.org coding tasks, it is remarkable that generalization to with tool use through code actions works so well. This ability to generalize to agentic jobs reminds of recent research study by DeepMind that reveals that RL generalizes whereas SFT memorizes, archmageriseswiki.com although generalization to tool use wasn’t investigated in that work.

Despite its capability to generalize to tool use, DeepSeek-R1 frequently produces long thinking traces at each action, compared to other designs in my experiments, limiting the usefulness of this model in a single-agent setup. Even simpler tasks in some cases take a long time to complete. Further RL on agentic tool usage, be it by means of code actions or not, could be one option to improve efficiency.

Underthinking

I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a thinking model often changes between different reasoning ideas without sufficiently checking out promising courses to reach an appropriate solution. This was a significant reason for excessively long thinking traces produced by DeepSeek-R1. This can be seen in the taped traces that are available for download.

Future experiments

Another typical application of reasoning models is to utilize them for preparing just, while using other designs for creating code actions. This could be a potential new function of freeact, if this separation of functions shows helpful for more complex tasks.

I’m likewise curious about how reasoning designs that currently support tool usage (like o1, o3, ...) carry out 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 also uses code actions, look interesting.