A Glimpse to the Future Large Reasoning Models

 Let's dive deeper into how large language models might evolve to large reasoning model:

1. Baseline Auto-Regressive Model: The Foundation – Predicting the Next Word with Context

At its core, the baseline autoregressive model is a sophisticated "next word prediction" engine. It doesn't just guess randomly; it uses the context of preceding words to make informed predictions. This context is captured through contextual embeddings. Imagine it like this: the model reads a sentence word by word, and with each word, it builds an understanding of the overall meaning and relationships between the words. This understanding is encoded in the contextual embeddings. These embeddings are then used to predict the most likely next word.

Here's a breakdown of the process:

• Tokenization: The input text is broken down into individual units – tokens. These can be words, subwords (parts of words), or even characters.

• Contextual Embedding Layer: This is where the magic happens. Each token is converted into a vector (a list of numbers) called a contextual embedding. Crucially, this embedding is not fixed; it depends on the surrounding words. So, the same word can have different embeddings depending on the context it appears in. For example, the word "bank" will have a different embedding in the sentence "I sat by the river bank" compared to "I went to the bank to deposit money." This context-sensitive embedding is what allows the model to understand nuances in language.

• Decoder Block: This part of the model takes the contextual embeddings as input and uses them to predict the probability of each possible next word/token. It considers all the words in its vocabulary and assigns a probability to each one, based on how well it fits the current context. The word with the highest probability is selected as the next word in the sequence.

Therefore, the baseline autoregressive model is fundamentally a context-driven next-word prediction engine. The contextual embeddings are central to this process, as they represent the model's understanding of the meaning and relationships between words in a given sequence.

2. Unrolled Auto-Regressive Model (Figure 2): The Sequential Process

This diagram illustrates the iterative nature of text generation. The model predicts one token at a time, and each prediction becomes the input for the next step. This "unrolling" visualizes how the model builds up a sequence token by token. The key takeaway here is that the model's understanding of the context evolves with each prediction. Early predictions can significantly influence later ones.

3. Auto-Regressive Model with Reasoning Tokens (Chain-of-Thought): Thinking Step-by-Step

This introduces the concept of explicit reasoning. By providing examples with intermediate reasoning steps during training, the model learns to generate its own reasoning steps before arriving at the final answer.

• Reasoning Tokens: These special tokens act as prompts to guide the model's thinking process. They can be natural language phrases or specific symbols that signal reasoning steps. For instance, reasoning tokens might start with "Therefore," "Because," or "Step 1:".

• Benefits of Chain-of-Thought: This approach improves performance on complex reasoning tasks by forcing the model to decompose the problem into smaller, more manageable steps. It also makes the model's reasoning more transparent and interpretable.

OpenAI's o1 model is one of those model trained with chain-of-thought reasoning.

4. Auto-Regressive Model with Reasoning Embedding: Implicit Reasoning

Here is the interesting part. Instead of having the reasoning tokens generated one by one, the context embedding of the reasoning token could possibly trained. So, Given the same embedding will generate the same token. If such model was trained, we can predict the next token efficiently without the overhead of generating explicit reasoning tokens.

• Reasoning Embedding Layer: This new layer learns to encode the essence of the reasoning process directly into the embeddings. Instead of explicitly generating reasoning steps, the model incorporates the learned reasoning patterns into its prediction process.

• Efficiency Gains: By eliminating the need to generate intermediate tokens, this approach reduces computational cost and speeds up text generation.

As large language models evolve into powerful reasoning engines, we stand on the brink of a new era in AI capabilities. From foundational autoregressive models to innovative reasoning embeddings, each step forward enhances the efficiency, interpretability, and complexity of what these models can achieve. By integrating explicit reasoning (reasoning tokens) and implicit reasoning (reasoning embeddings) mechanisms, the future promises not only faster and more accurate text generation but also models capable of deeper understanding and problem-solving.