Sakana AI's Latest Release: Evolutionary Optimization of Model Merging Recipes

🐂 🌾 Arxiv Dives with
1 Apr 202440:22

TLDRThis episode of Archive Dives explores the innovative paper by Sakana AI on 'Evolutionary Optimization of Model Merging Recipes.' The paper proposes an evolutionary algorithm to merge existing open-source AI models, enhancing performance without extensive retraining. The method's success on math reasoning tasks in Japanese, using the merge kit repository, demonstrates a promising approach to model optimization with minimal computational resources, offering exciting possibilities for AI development.


  • 🔬 The paper from Sakana AI discusses 'evolutionary optimization of model merging recipes', suggesting a method to improve AI models by merging them using evolutionary algorithms.
  • 🤖 The idea is to leverage existing open-weight models, combining them to create new models that are more efficient than training from scratch or fine-tuning existing ones.
  • 🛠️ Sakana AI's approach involves using an evolutionary algorithm to breed the best-performing models and discard the less effective ones, aiming to optimize model performance with minimal computational resources.
  • 🔗 The paper references 'merge kit', a GitHub project that facilitates the merging of models from Hugging Face in various ways, which is integral to the discussed methodology.
  • 🧬 The merging of models is described as somewhat of a 'black art', with the paper suggesting that an evolutionary algorithm can provide a systematic approach to discovering effective model combinations.
  • 📈 The paper includes evaluations that show significant improvements in model performance through merging, even surpassing some large models with much smaller ones.
  • 🌐 The method's effectiveness is demonstrated through experiments on math reasoning tasks in Japanese, using a dataset similar to the GSM 8K dataset in English.
  • 🔍 The paper explores different types of model merging, including parameter space merging and data flow space merging, both of which contribute to the overall performance of the merged model.
  • 📊 The use of the CMA-ES algorithm (Covariance Matrix Adaptation Evolutionary Strategy) is highlighted for its suitability in optimizing models without a predefined set of actions.
  • 🚀 The potential for applying these merging techniques to other areas such as visual language models and diffusion language models is mentioned, indicating the broad applicability of the approach.
  • 💡 The paper concludes with a discussion on the limitations of the method, including the need to understand the original training data and the risk of inheriting both strengths and weaknesses from the merged models.

Q & A

  • What is the main topic of the paper discussed in the video?

    -The main topic of the paper is the 'evolutionary optimization of model merging recipes', which explores the idea of using an evolutionary algorithm to merge open weights models to improve performance with minimal compute resources.

  • What is Sakana AI's approach to model merging?

    -Sakana AI's approach involves using an evolutionary algorithm to breed open weights models, keeping the fittest and discarding the others, instead of continually training models from scratch or fine-tuning them for specific use cases.

  • What is Oxen AI's role in the context of this paper?

    -Oxen AI is building a toolchain to help with the collaboration and iteration of machine learning datasets, which could be used to manage the experiments discussed in the paper.

  • What is the significance of the GitHub project 'merge kit' mentioned in the video?

    -The 'merge kit' project on GitHub is significant as it allows users to merge any two models from Hugging Face using various techniques, which is central to the model merging process discussed in the paper.

  • How does the paper address the challenge of model merging being somewhat of a 'black art'?

    -The paper introduces a systematic approach to discovering new model combinations using an evolutionary algorithm, which aims to reduce the reliance on human intuition in the model merging process.

  • What are some of the model merging techniques mentioned in the video?

    -Some of the techniques mentioned include linear weighting, slurp (spherical linear interpolation), Ties merging, and DARE (zeroing out small differences between models).

  • What is the role of the CMA-ES algorithm in the paper's approach to model merging?

    -The CMA-ES (Covariance Matrix Adaptation Evolutionary Strategy) algorithm is used to optimize the model merging process by creating a population of models, evaluating them, breeding the best performing ones, and discarding the rest over multiple generations.

  • What is the significance of the 'data flow space' and 'parameter space' in model merging?

    -In model merging, 'data flow space' refers to the inference path each token takes through the network, while 'parameter space' refers to the blending of model weights. The paper suggests that merging in both spaces can lead to improved model performance.

  • How did the paper's experiments perform in terms of accuracy?

    -The experiments showed significant improvements in accuracy, with some models achieving over 55% accuracy on math reasoning tasks in Japanese, which was a substantial increase from the initial 9% accuracy.

  • What are some of the limitations mentioned in the paper regarding the evolutionary optimization of model merging?

    -Some limitations include the need to know the original training data of the models, the potential for inheriting weaknesses along with strengths, and the challenge of ensuring that the models perform well across a variety of tasks, not just the ones they were optimized for.



📚 Introduction to Archive Dives with Oxen AI

The script opens with a warm welcome to 'Archive Dives', a weekly series hosted by Oxen AI that focuses on exploring intriguing research papers in the field of machine learning and AI. The purpose is to extract insights that can be applied to one's own work. Newcomers are encouraged to introduce themselves in the chat, and the session is conducted live every Friday, attracting a global audience. The host mentions a Discord community for further discussions post-session. Today's highlight is a fascinating paper from Sakana AI on 'evolutionary optimization of model merging recipes', which discusses the concept of combining existing open-weight models using evolutionary algorithms to improve performance without extensive training from scratch.


🧬 Evolutionary Model Merging Techniques

This paragraph delves into the different types of model merging techniques such as linear weighting, 'slurp' (spherical linear interpolation), 'ties' merging, and 'dare' merging. The speaker discusses the open-source nature of the research, with all code and weights available on GitHub, promoting open science. The paper explores the idea of improving model performance with minimal compute resources by leveraging existing trained models. The merging process is described as somewhat of a black art, with the paper providing evaluations that showcase significant performance improvements. The talk also touches on the 'merge kit' GitHub project, enabling the merging of any two models from Hugging Face in various ways.


🔬 Framework for Model Merging Performance

The speaker outlines the goal of creating a framework for model merging that surpasses the performance of individual models. Two primary approaches to merging are discussed: merging in parameter space, likened to blending colors, and merging in data flow space, which involves copying entire blocks from one model to another. The paper uses a variety of techniques, sometimes merging in parameter space and sometimes in data flow space, to create models that combine both interpolated blocks and direct copies. The evolutionary algorithm is introduced as a method to discover new model combinations systematically, rather than relying solely on human intuition.


⚙️ CMA-ES Algorithm and Model Evaluation

The script explains the use of the CMA-ES (Covariance Matrix Adaptation Evolutionary Strategy) algorithm for numerical optimization in the context of model merging. The process involves creating a population of models, evaluating them, breeding model weights to create new population members, and selectively keeping the best-performing models through generations. The paper's experiments were conducted using a Japanese math dataset, and the results showed significant accuracy improvements. The speaker also discusses the possibility of applying these techniques to other areas such as visual language models and generative models.


📈 Model Merging Results and Limitations

The paragraph discusses the results of the model merging experiments, which showed substantial increases in accuracy using an evolutionary algorithm. The paper mentions a population size of 128 and 100 generations for the experiments. The speaker highlights the potential of these techniques to be applied to other models and tasks, but also acknowledges the limitations, such as the difficulty of knowing the original training data and the risk of inheriting weaknesses along with strengths from the merged models.


🤖 Discussion on Evolutionary Model Merging

This section includes a discussion on the efficiency and potential of evolutionary model merging. The speaker expresses concerns about the method being inefficient and requiring significant computational resources. There's a mention of the need for constraints to make the search space more manageable. The conversation also touches on the possibility of having humans in the loop for scoring models and the idea of using model merging for different types of models beyond language models.


🏥 GDPR and Data Security Concerns

The speaker addresses a question about working with sensitive data, such as GDPR-related health data, and the need to ensure security when using platforms like Oxen. The response includes information about private deployments and repositories to safeguard against data breaches and hacking, emphasizing the importance of meeting security requirements for sensitive data.


🎉 Conclusion and Future Engagement

The script concludes with an invitation for further engagement, encouraging participants to share paper ideas in the Discord community and to sign up for Oxen as a beta user. The host expresses a desire to meet participants at an upcoming event in San Francisco and ends the session with music and applause.



💡Evolutionary Optimization

Evolutionary optimization refers to a method of improving models or algorithms inspired by the process of natural evolution. In the context of the video, it is used to enhance machine learning models by mimicking the survival of the fittest concept, where the best-performing model configurations are selected and 'bred' to create new, potentially improved models. This is a central theme of the paper discussed in the video, where models are merged and optimized using evolutionary strategies.

💡Model Merging

Model merging is the process of combining two or more pre-trained machine learning models to create a new model that potentially leverages the strengths of the original models. In the video, this concept is explored through the use of an evolutionary algorithm to systematically discover and merge models in ways that may improve performance on specific tasks, such as math reasoning in Japanese.

💡Open Weights Models

Open weights models are machine learning models where the trained parameters (weights) are publicly available for use, often for research or experimentation purposes. The video discusses the idea of using these open models as a starting point for breeding and merging, rather than training models from scratch, to save computational resources.

💡Evolutionary Algorithm

An evolutionary algorithm is a subset of evolutionary computation and artificial intelligence that uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. In the video, the CMA-ES algorithm, a type of evolutionary strategy, is mentioned as the method used for optimizing the model merging process.

💡Merge Kit

Merge Kit is a project mentioned in the video that allows for the merging of any two models from Hugging Face through various methods. It is an example of a tool that can be used to facilitate the process of model merging, which is a key part of the research discussed in the video.

💡Parameter Space Merging

Parameter space merging is a technique where the weights of two models are combined, such as through averaging or more sophisticated methods like SLERP (Spherical Linear Interpolation). The video explains this as one of the merging approaches used in the evolutionary optimization process to create new models with potentially improved performance.

💡Data Flow Space

Data flow space refers to the path that data takes as it is processed through a model. In the context of model merging, the video describes how this concept is used to select and merge parts of models that are particularly effective for certain types of data or tasks, aiming to optimize the new merged model's performance.

💡CMA-ES Algorithm

CMA-ES stands for Covariance Matrix Adaptation Evolution Strategy, which is an advanced evolutionary algorithm used for numerical optimization. The video script explains its application in the context of model merging, where it is used to iteratively improve model configurations by evaluating performance and breeding the best-performing models.

💡Franken Merges

A Franken merge is a term used in the video to describe the process of combining layers or architectures from different models in a non-standard way, akin to creating a 'Frankenstein's monster' of machine learning models. This approach is mentioned as a trial-and-error method that surprisingly yields effective results.

💡Math Reasoning

Math reasoning is a specific application area discussed in the video where the evolutionary optimization of model merging is applied. The video describes how the merging technique was tested on a Japanese math dataset, demonstrating significant improvements in accuracy through the merging process.


Sakana AI's latest research explores the concept of 'evolutionary optimization of model merging recipes', aiming to improve AI models by merging existing open weights models.

The idea involves using an evolutionary algorithm to breed the best performing models together, keeping the fittest and discarding the rest.

Oxen AI is building tools to help with machine learning collaboration and iteration, potentially useful for running experiments on model merging.

The paper discusses the use of GitHub's 'merge kit' project for merging models from Hugging Face in various ways.

Merging models is currently somewhat of a black art, with the paper questioning the surprising effectiveness of this technique.

The paper evaluates the performance improvements of merged models, with results that are described as mind-blowing.

Different merging techniques such as linear weighting, slurp, ties merging, and dare are discussed in the paper.

The paper references the open LLM leaderboard, indicating that many top models are the result of model merging.

All code and weights from the experiments are open source, promoting open science.

The paper introduces the concept of merging in parameter space (PS) and data flow space (DFS), offering two orthogonal approaches to model merging.

The CMA-ES algorithm is used for numerical optimization, providing a strategy for model merging without a ground truth set of actions.

The evolutionary algorithm creates a population of models, evaluates them, breeds new models, and iteratively improves performance.

Experiments show significant accuracy improvements in math reasoning tasks when using merged models.

The paper suggests that evolutionary algorithms could be applied to other areas such as visual language models and diffusion language models.

The paper acknowledges limitations, including the need for models to be evaluated on datasets they were not originally trained on.

The evolutionary merging technique may also inherit the weaknesses of the original models, necessitating additional refinement steps.

The discussion includes thoughts on human-in-the-loop scoring and the potential for distributed model evaluation through community efforts.

The potential for using Alpha geometry to create constraints for the search space in evolutionary algorithms is mentioned.

Security considerations for working with sensitive datasets like GDPR are discussed, with options for private deployments and repositories.