I ran a fast experiment examining how DeepSeek-R1 performs on agentic tasks, despite not supporting tool use natively, and I was rather amazed by preliminary results. This experiment runs DeepSeek-R1 in a single-agent setup, utahsyardsale.com where the model not just prepares the actions however 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% absolute, from 53.1% to 65.6% appropriate, 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 used). You can discover more evaluation details here.

Approach

DeepSeek-R1’s strong coding capabilities allow it to function as an agent without being clearly trained for tool usage. By permitting the model to create actions as Python code, it can flexibly connect with environments through code execution.

Tools are carried out as Python code that is in the timely. This can be a simple function meaning or a module of a larger bundle - any legitimate Python code. The design then generates code actions that call these tools.

Results from performing these actions feed back to the model as follow-up messages, driving the next actions until a last response is reached. The agent framework is a basic iterative coding loop that mediates the conversation between the design and its environment.

Conversations

DeepSeek-R1 is used as chat design in my experiment, where the design autonomously pulls additional context from its environment by using tools e.g. by utilizing an online search engine or fetching data from websites. This drives the discussion with the environment that continues till a final answer is reached.

On the other hand, o1 models are known to perform poorly when utilized as chat models i.e. they do not try to pull context throughout a conversation. According to the linked post, o1 designs perform best when they have the full context available, with clear guidelines on what to do with it.

Initially, I also attempted a complete context in a single timely method at each action (with arise from previous steps consisted of), but this resulted in considerably lower ratings on the GAIA subset. Switching to the conversational approach explained above, I had the ability to reach the reported 65.6% efficiency.

This raises an intriguing question about the claim that o1 isn’t a chat model - possibly this observation was more pertinent to older o1 models that lacked tool usage capabilities? After all, isn’t tool usage support an important system for enabling designs to pull extra context from their environment? This conversational approach certainly appears reliable for DeepSeek-R1, though I still need to perform comparable experiments with o1 models.

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 tasks advises of current research by DeepMind that shows that RL generalizes whereas SFT remembers, although generalization to tool use wasn’t examined in that work.

Despite its ability to generalize to tool use, DeepSeek-R1 frequently produces very long thinking traces at each action, compared to other models in my experiments, restricting the usefulness of this model in a single-agent setup. Even simpler tasks sometimes take a long time to finish. Further RL on agentic tool usage, be it through code actions or not, might be one option to improve efficiency.

Underthinking

I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a thinking model often switches in between various thinking thoughts without adequately exploring promising courses to reach a proper solution. This was a major factor for extremely 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 designs is to utilize them for planning just, while utilizing other models for generating code actions. This could be a potential brand-new function of freeact, if this separation of roles shows helpful for fraternityofshadows.com more complex tasks.

I’m likewise curious about how thinking models that already support tool usage (like o1, o3, …) carry out in a single-agent setup, with and without generating code actions. Recent advancements like OpenAI’s Deep Research or Hugging Face’s open-source Deep Research, which also utilizes code actions, look interesting.