on the Bootstraps in Generative AI

Generative AI, especially through the development of large language models (LLMs), is undergoing a bootstrap effect reminiscent of the early evolution of computer compilers. Just as the first compilers laid the foundation for all future programming languages, today's language models are breaking new ground, becoming both the tools and the content creators for their own advancement. This self-reinforcing loop is driving progress at the unprecedented pace one can observe today, reshaping our understanding of AI's potential.

The Bootstrap Effect: Lessons from Statistics to Compiler Development

In statistics, bootstrapping is a resampling technique used to estimate the distribution of a statistic by drawing repeated samples, with replacement, from the original dataset. Introduced in laste 70s, the method allows for the approximation of standard errors, confidence intervals, and other statistical measures without relying on strong parametric assumptions. By generating numerous "bootstrap samples," analysts can assess the variability and robustness of estimates, even with limited data. This original bootstrap concept mirrors what we will observe in early development of compiler and the recent emergence of AI.

To understand this bootstrap effect in AI, it can be helpful to look back at the history of compiler development. The concept of compilers dates back to the early 1950s, with Grace Hopper's work on the A-0 System often cited as the first compiler . This laid the groundwork for the development of FORTRAN and subsequent programming languages.

The first compilers were manually written for specific programming languages—painstakingly designed to translate human-readable code into machine-executable instructions. The creation of Fortran in the 1950s marked a critical milestone, as it introduced the concept of automated code translation, significantly reducing the need for manual assembly coding.

Once the first compiler was built, it became possible to write compilers for other languages more efficiently, using the structure of the original one as a foundation. Each new language benefited from the insights gained during the creation of previous compilers. In a sense, compilers began to bootstrap each other, making it easier and faster to create new programming languages.

This same dynamic is now unfolding in generative AI. The first language models were trained on large amounts of human-generated data, but as these models improved, they began generating data themselves. Today, we are seeing models generate or re-write the data used to create or fine-tune subsequent models, creating a self-sustaining loop of continuous improvement—what can be called the bootstrap effect in AI.

Another example of this phenomenon can be observed in the area of verbal reinforcement learning, particularly in natural language processing. Early language models required vast amounts of human-labeled data to learn linguistic patterns and improve their performance. However, as models became more sophisticated, they begin to rely on feedback and self-generated data, where one AI model's output is used as training signal for another. This self-reinforcing loop is analogous to human verbal learning, where feedback continuously refines the learner's responses. Over time, models trained in this way can autonomously generate increasingly coherent and contextually accurate outputs, accelerating their own development through reinforcement. The recently proposed reflection mechanism seems to verify this hypothesis.

How LLMs Are Bootstrapped

At the core of this effect is a feedback loop between data generation and model refinement. Early LLMs were trained on vast, unfiltered datasets like the internet, learning to predict and generate human-like text. But as models like GPT-4 or Claude improved, they started producing cleaner, more focused datasets that can be fed back into training processes. This allows newer models to train on high-quality data, further refining their performance.

The same principle applies to self-supervised learning, where a model can be trained without explicit human labels, using the context of the input data itself for learning. These improvements mean that the line between human and machine-generated training data is blurring, with models contributing to their own refinement.

The importance of understanding the bootstrapping process in LLMs becomes even more critical when considering that companies producing these models, even those open-sourcing them, often do not fully disclose their data collection and processing practices. This observation seems to confirms the importance of this process while raising concerns about the origins, quality, and ethical implications of the data used.

Multimodality, Beyond Text

A significant recent development in generative AI is multimodal learning, where models are capable of processing and generating data from different modalities such as text, images, and audio. This step represents another dimension of the bootstrap effect, as multimodal models enhance their capabilities by learning from a diverse array of data types, improving how they handle complex, real-world tasks.

Early AI models, such as GPT and BERT, were strictly focused on text. But with the introduction of models like CLIP (Contrastive Language-Image Pretraining) and DALL·E, AI began integrating visual data into its language models. CLIP learns to align text and image embeddings, making it possible for a machine to understand visual contexts through language, and vice versa . DALL·E, meanwhile, generates images from textual prompts, showcasing AI's ability to produce creative outputs from multiple modalities .

These multimodal models are building on the same bootstrap principle: by generating text based on images, and using images to refine language understanding, the models improve across both dimensions. Early models like Image-Conditioned Masked Language Models (ICMLM) have emerged as a way to integrate visual information into text generation, producing richer, more contextualized outputs that are grounded in both language and imagery .

Recent advancements have further pushed the boundaries of multimodal capabilities, demonstrating improved performance across a wide range of tasks including visual reasoning and code generation .

the Risk of Statistical Collapse

Despite the promise of this self-improving cycle, one have warned about statistical collapse, the risk that AI models, if trained predominantly on synthetic data generated by earlier models, might start degrading in quality over time. In theory, over-reliance on AI-generated data could cause models to lose touch with the nuances of real-world information, creating a feedback loop of diminishing accuracy.

However, in practice, the collaborative nature of LLMs with human users mitigates this risk. Modern models are not isolated in a loop of synthetic data; they continuously learn from user interactions, incorporating real-world feedback and adapting to new inputs. One hope is that this conversation between models and users serves as a safeguard against the theoretical collapse, ensuring that AI remains connected to actual, meaningful data from the world.

Advancing AI Reasoning: Chain of Thought and Reinforcement Learning

One of the most exciting areas of AI development is the growing focus on reasoning and problem-solving. This is where techniques like Chain of Thought (CoT) reasoning and Reinforcement Learning and Monte Carlo Tree Search (MCTS) come into play, pushing AI models beyond simple text prediction into more advanced reasoning tasks.

As I frequently insisted in the past, Large Language models are not about text but all about reasoning and common-sense.

Chain of Thought reasoning helps models break down complex problems into manageable, logical steps. This process enables the AI to solve multi-step reasoning tasks more effectively, improving not only the accuracy but also the transparency of its thought process .

Monte Carlo Tree Search, which has been widely used in game AI, is now being adapted to assist LLMs in navigating vast decision spaces, helping the model explore different reasoning paths before selecting the most promising solution .

These advancements in reasoning capabilities represent a major leap forward in AI. As a researcher and leader in reasoning and common sense for differentiable models research since 2008, I have worked on models that tackle reasoning in both structured and unstructured contexts.

Looking Ahead: A Self-Sustaining Future of Capabilities and Services

The bootstrap effect in AI, amplified by multimodal capabilities and emerging reasoning tools, is propelling us into a future where AI models can refine, improve, and even create their own successors. From compiler development to self-improving LLMs, the same principle holds: each generation learns from the last, accelerating progress.

As models increasingly integrate text, images, and other data types, and as reasoning frameworks advance, hopefully AI will continue to bootstrap itself to greater heights. Reflecting on my contributions in gated memory, reasoning, common sense, and multimodality, it's clear that we are entering a new era. The potential for LLMs to develop reasoning, understand complex problems, and engage across different data modalities is only beginning to unfold.

About the author: Julien Perez is an associate professor at EPITA: Ecole d'Ingénieurs en Informatique and member of the AI team of the Laboratoire de Recherche de l'EPITA (LRE), with a focus on natural language processing, machine learning, and reasoning systems.

Julien has made contributions to several areas of AI research, including: Natural language processing and understanding, Machine learning, particularly in the context of language models, Reasoning and common sense in AI systems, Multimodal learning, integrating text and visual information, Dialogue systems and conversational AI and more recently Robot Learning.

His most cited works include research on attention networks for natural language inference, dialogue state tracking using memory networks, and image-conditioned masked language models. Julien has been actively publishing research in these domains since at least 2008, with his work appearing in top-tier conferences and journals in the field of AI and computational linguistics.