A HEGELIAN SYNTHESIS OF TRANSFORMERS AND RL
Reasoning models that combine next-token prediction with reinforcement learning represent a shift in expectations about AI progress, challenging the assumption that scaling pre-training alone would be sufficient to achieve sophisticated, generalized intelligence. For years, the dominant view held that increasing the scale of language model pre-training would naturally lead to emergent reasoning capabilities. The success of models like GPT-3 in 2020 and its successors reinforced this belief, as ever-larger models trained on vast text corpora demonstrated surprising fluency and problem-solving ability.
In late 2024, OpenAI’s o1 introduced a model designed not just to predict, but to reason, verify, and refine its own outputs. Its successor, o3, followed months later, demonstrating a significant leap forward in logical problem-solving. In early 2025, DeepSeek R1, developed by a research collective in China, emerged as an open-source alternative, emphasizing large-scale RL as a key driver of its reasoning capabilities.
Hegel’s dialectics provides a useful conceptual framework for understanding the resurgence of RL in AI, where progress emerges from the synthesis of competing forces. Once seen as rival paradigms, Transformers and RL are now converging to unlock a new frontier in artificial intelligence. Hegel’s dialectic describes the evolution of ideas as a three-step process, namely thesis, antithesis, and synthesis. A prevailing idea (thesis) is challenged by its opposite (antithesis), and through their conflict, a new, higher-order understanding emerges (synthesis). In the context of AI, RL, once overshadowed by the dominance of transformer-based models, has re-emerged as an essential component, driving advancements in reasoning.
The Rise and Fall of RL
RL was the topic du jour in the mid-2010s, dominating AI research and driving groundbreaking advancements. Deep RL algorithms mastered Atari games and defeated world champions at Go, sparking speculation that general AI might emerge from these techniques.
However, despite its promise, RL had fundamental limitations. It thrived in structured environments with clear rewards but faltered in open-ended, real-world tasks. Unlike AlphaGo, which trained on 4.9 million self-play games, most real-world problems lack such well-defined learning landscapes. Rewards are sparse, environments unbounded, and RL agents often need an astronomical number of trials to learn effective policies.
Beyond these technical hurdles, RL faced structural disadvantages as the industry shifted toward transformer-based architectures. Training RL agents was computationally expensive, requiring millions of iterations, while self-supervised learning scaled effortlessly across vast text and image datasets. RL models also suffered from sensitivity to hyperparameters, instability during training, and poor transferability, stark contrasts to the robustness and generalization of large transformers.
The Rise of Transformers and Self-Supervised Learning
As RL hit its bottlenecks, a new paradigm emerged, namely self-supervised learning with transformers. Researchers found that training massive neural networks on internet-scale text corpora yielded surprisingly broad capabilities. Models like BERT and GPT-2 showed that next-word prediction alone could produce sophisticated linguistic representations. By 2020, GPT-3 with 175 billion parameters stunned the world with its emergent reasoning, translation, and question-answering abilities, despite lacking explicit task-specific training.
The industry quickly recognized that scaling model size and data was a more tractable path to intelligence than manually designing RL reward functions. Transformer-based models were highly adaptable, easily fine-tuned for domain-specific tasks or prompted for zero-shot learning. Meanwhile, RL remained unstable, with agents that were difficult to train and poor at generalizing beyond their training distribution.
After early investments in game-playing agents and robotics, OpenAI shifted focus to large-scale language models. ChatGPT and GPT-4 cemented transformer-based AI as dominant, while RL was largely reduced to fine-tuning methods like Reinforcement Learning from Human Feedback (RLHF).
RL Built on the Shoulders of Transformers
The resurgence of RL in reasoning models stemmed from the key realization that it does not need to start from scratch. Instead, it could refine pre-trained language models that already possess vast knowledge, linguistic fluency, and logical structure.
Historically, RL struggled with the sheer vastness of the search space in complex reasoning tasks. For instance, solving a multi-step math proof or writing a program presents a combinatorial explosion of action sequences. A pure RL approach would be akin to a person randomly typing characters in the hope of producing a working algorithm.
However, pre-training fundamentally alters this dynamic. Modern reasoning models begin with linguistic and logical priors encoded through self-supervised learning, allowing RL to operate within a much more structured search space. The model already understands basic algebra, programming syntax, and common reasoning strategies. Thereafter, RL refines and orchestrates these capabilities into structured, iterative reasoning.
In this synthesis, large pre-trained models provide the raw "vocabulary" of thoughts, while RL constructs the "grammar" of reasoning to guide the model in how to assemble and apply its knowledge effectively. While AlphaGo had to learn Go strategy from millions of self-play games, reasoning models inherit human civilization’s accumulated knowledge and focus on optimizing its application.
AlphaZero for Everything
The implications of this synthesis extend far beyond technical architecture. As I have described elsewhere, current AI development increasingly resembles a “meta-AlphaZero”. Given sufficient resources and research efforts, modern methods would exhaust any formal evaluation framework by systematically turning explicit rules and objectives into optimization targets. Just as AlphaZero mastered chess by refining its gameplay around well-defined success criteria, today’s large pre-trained models, augmented by RL, appear capable of mastering any domain where performance can be formally specified and measured.