1 Exploring DeepSeek-R1's Agentic Capabilities Through Code Actions

Adrianne Foveaux edited this page 2025-02-12 08:21:53 +01:00

I ran a quick experiment examining how DeepSeek-R1 performs on agentic jobs, despite not supporting tool use natively, and I was quite impressed by initial outcomes. This experiment runs DeepSeek-R1 in a single-agent setup, where the model not just plans the actions however also develops the actions as executable Python code. On a subset1 of the GAIA validation split, visualchemy.gallery DeepSeek-R1 outperforms Claude 3.5 Sonnet by 12.5% absolute, from 53.1% to 65.6% proper, 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 use few-shot examples, prevent adding a system timely, and set the temperature level to 0.5 - 0.7 (0.6 was used). You can discover additional evaluation details here.

Approach

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

Tools are implemented as Python code that is consisted of straight in the timely. This can be a simple function meaning or a module of a larger plan - any valid Python code. The model then generates code actions that call these tools.

Results from these actions feed back to the model as follow-up messages, driving the next steps till a last response is reached. The representative framework is a simple iterative coding loop that moderates the conversation between the design 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 using tools e.g. by utilizing an online search engine or bring information from web pages. This drives the conversation with the environment that continues up until a final answer is reached.

On the other hand, o1 models are known to carry out badly when utilized as chat models i.e. they don't attempt to pull context during a conversation. According to the connected short article, o1 models carry out best when they have the full context available, with clear guidelines on what to do with it.

Initially, I also tried a full context in a single prompt approach at each step (with arise from previous steps included), however this caused considerably lower scores on the GAIA subset. Switching to the conversational technique explained above, I was able to reach the reported 65.6% performance.

This raises an intriguing question about the claim that o1 isn't a chat model - possibly this observation was more relevant to older o1 designs that did not have tool use capabilities? 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 seems reliable 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 usage through code actions works so well. This ability to generalize to agentic tasks reminds of recent research study by DeepMind that shows that RL generalizes whereas SFT remembers, although generalization to tool use wasn't investigated because work.

Despite its capability to generalize to tool use, DeepSeek-R1 frequently produces extremely long thinking traces at each action, compared to other models in my experiments, limiting the usefulness of this design in a single-agent setup. Even simpler tasks often take a long period of time to complete. Further RL on agentic tool use, be it by means of 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 design often switches in between different thinking ideas without adequately exploring appealing paths to reach a proper option. This was a significant reason for overly 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 use them for preparing only, while utilizing other models for producing code actions. This might be a potential new feature of freeact, if this separation of functions shows beneficial for hikvisiondb.webcam more complex jobs.

I'm also curious about how reasoning designs that already support tool use (like o1, o3, ...) carry out in a single-agent setup, with and annunciogratis.net 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 interesting.