The Free Transformer (Oct 2025)

Title: The Free Transformer (Oct 2025)
Link: http://arxiv.org/abs/2510.17558v1
Date: October 2025

Summary:
This paper introduces the Free Transformer, an extension of the decoder-only Transformer that conditions its generative process on learned, unsupervised random latent variables. Leveraging a conditional Variational Autoencoder (VAE) formulation, the model allows for explicit latent decisions, addressing the limitations of purely autoregressive models where such concepts remain implicit. The proposed architecture integrates a single additional non-causal Transformer block with minimal computational overhead. Experimental evaluations on 1.5B and 8B parameter models demonstrate substantial performance improvements across various downstream benchmarks, particularly in tasks requiring reasoning such as HumanEval+, MBPP, and GSM8K, indicating a stronger inductive bias.

Key Topics:
Decoder Transformer
Variational Autoencoder (VAE)
Latent Variables
Conditional Generative Models
Auto-regressive Models
Large Language Models (LLMs)
Code Generation
Reasoning Benchmarks
Free Bits Method
Model Architecture

Chapters:
00:00 - Free Transformer Overview
01:42 - Autoregressive Model Limitations
04:15 - VAE Architectural Integration
06:01 - Free Transformer Architecture
07:52 - Designing Latent Variable Z
09:07 - Binary Mapper and Training
12:05 - Analyzing Latent Variable Learning
13:37 - Benchmark Results and Gains
15:55 - Future of Latent Planning

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