I ran a fast experiment investigating how DeepSeek-R1 carries out on agentic jobs, despite not supporting tool use natively, and I was quite amazed by initial results. This experiment runs DeepSeek-R1 in a single-agent setup, clashofcryptos.trade where the design not only plans the actions but also formulates the actions as executable Python code. On a subset1 of the GAIA validation split, DeepSeek-R1 outperforms Claude 3.5 Sonnet by 12.5% outright, from 53.1% to 65.6% correct, and other models by an even larger margin:

The experiment followed design use standards from the DeepSeek-R1 paper and the design 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 more examination details here.

Approach

DeepSeek-R1’s strong coding abilities enable it to function as a representative without being clearly trained for tool usage. By enabling the model to produce actions as Python code, gdprhub.eu it can flexibly communicate with environments through code execution.

Tools are carried out as Python code that is consisted of straight in the prompt. This can be an easy function meaning or a module of a larger plan - any legitimate Python code. The model then produces code actions that call these tools.

Arise from executing these actions feed back to the model as follow-up messages, driving the next steps till a final answer is reached. The representative framework is a basic iterative coding loop that moderates the conversation between the model and its environment.

Conversations

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

On the other hand, o1 models are known to perform inadequately when utilized as chat models i.e. they don’t try to pull context during a discussion. According to the connected short article, o1 models carry out best when they have the full context available, with clear instructions on what to do with it.

Initially, I likewise attempted a complete context in a single timely technique at each action (with arise from previous steps consisted of), wavedream.wiki 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 a fascinating concern about the claim that o1 isn’t a chat design - possibly this observation was more appropriate to older o1 models that did not have tool use capabilities? After all, isn’t tool use support an essential mechanism for enabling models to pull extra context from their environment? This conversational method certainly seems efficient for DeepSeek-R1, though I still require to conduct comparable explores o1 designs.

Generalization

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

Despite its ability to generalize to tool usage, DeepSeek-R1 frequently produces extremely long thinking traces at each action, compared to other designs in my experiments, restricting the effectiveness of this design in a single-agent setup. Even simpler tasks often take a very long time to finish. Further RL on agentic tool usage, be it via code actions or wavedream.wiki not, could be one alternative to improve performance.

Underthinking

I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a thinking design frequently changes between various reasoning ideas without adequately checking out promising paths to reach an appropriate solution. This was a major reason for extremely long thinking 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 utilize them for preparing only, while utilizing other designs for generating code actions. This might be a possible brand-new feature of freeact, if this separation of functions proves helpful for more complex jobs.

I’m likewise curious about how reasoning models that currently support tool use (like o1, [smfsimple.com](https://www.smfsimple.com/ultimateportaldemo/index.php?action=profile