I ran a quick experiment examining how DeepSeek-R1 carries out on agentic tasks, despite not supporting tool usage natively, and I was quite impressed by initial outcomes. This experiment runs DeepSeek-R1 in a single-agent setup, where the model not only plans the actions but likewise formulates the actions as executable Python code. On a subset1 of the GAIA validation split, DeepSeek-R1 outshines Claude 3.5 Sonnet by 12.5% outright, from 53.1% to 65.6% right, and other models by an even larger margin:
The experiment followed design use standards from the DeepSeek-R1 paper and the model card: Don't use few-shot examples, avoid adding a system prompt, and set the temperature to 0.5 - 0.7 (0.6 was used). You can discover additional examination details here.
Approach
DeepSeek-R1's strong coding abilities allow it to serve as an agent without being explicitly trained for tool usage. By allowing the design to generate actions as Python code, it can flexibly interact with environments through code execution.
Tools are implemented as Python code that is consisted of straight in the prompt. This can be a basic function meaning or a module of a larger package - any valid Python code. The design then produces code actions that call these tools.
Results from carrying out these actions feed back to the model as follow-up messages, driving the next steps till a final response is reached. The representative framework is a simple iterative coding loop that mediates the discussion in between the design and its environment.
Conversations
DeepSeek-R1 is utilized as chat design in my experiment, where the design autonomously pulls extra context from its environment by using tools e.g. by using a search engine or fetching data from web pages. This drives the discussion with the environment that continues up until a last response is reached.
On the other hand, o1 designs are understood to carry out improperly when utilized as chat designs i.e. they don't attempt to pull context during a discussion. According to the connected short article, o1 models perform best when they have the full context available, with clear directions on what to do with it.
Initially, I likewise tried a complete context in a single timely approach at each action (with arise from previous steps included), however this led to considerably lower scores on the . Switching to the conversational method explained above, I had the ability to reach the reported 65.6% efficiency.
This raises an interesting question about the claim that o1 isn't a chat design - maybe this observation was more pertinent to older o1 models that lacked tool use abilities? After all, isn't tool use support an important system for enabling designs to pull additional context from their environment? This conversational approach certainly appears effective for DeepSeek-R1, though I still need to perform comparable try outs o1 models.
Generalization
Although DeepSeek-R1 was mainly trained with RL on math and coding jobs, it is impressive that generalization to agentic tasks with tool use by means of code actions works so well. This ability to generalize to agentic tasks advises of recent research study by DeepMind that shows that RL generalizes whereas SFT remembers, although generalization to tool use wasn't investigated in that work.
Despite its capability to generalize to tool use, DeepSeek-R1 often produces long thinking traces at each step, compared to other models in my experiments, limiting the usefulness of this model in a single-agent setup. Even simpler tasks sometimes take a very long time to finish. Further RL on agentic tool use, be it through code actions or not, could be one choice to improve effectiveness.
Underthinking
I likewise observed the underthinking phenomon with DeepSeek-R1. This is when a thinking design frequently changes between different thinking thoughts without adequately exploring promising paths to reach a proper service. This was a significant factor for overly long thinking traces produced by DeepSeek-R1. This can be seen in the tape-recorded traces that are available for download.
Future experiments
Another common application of reasoning designs is to use them for preparing just, while using other models for producing code actions. This could be a possible new function of freeact, valetinowiki.racing if this separation of roles proves beneficial for more complex tasks.
I'm likewise curious about how reasoning designs that currently support tool use (like o1, o3, ...) carry out in a single-agent setup, with and without creating code actions. Recent developments like OpenAI's Deep Research or Hugging Face's open-source Deep Research, which likewise utilizes code actions, look fascinating.
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Exploring DeepSeek-R1's Agentic Capabilities Through Code Actions
Abigail Waugh edited this page 2025-02-11 00:09:31 +07:00