Eliminating Backpropagation in LLM Training

LoCA (Local Credit Assignment) addresses the high memory and computational costs associated with traditional backpropagation in large language model (LLM) fine-tuning. By shifting to a forward-only training paradigm, LoCA removes the need to store massive activation buffers required for the backward pass, which is the primary bottleneck in training large-scale models on consumer or resource-constrained hardware.

The LoCA Mechanism: One-Shot Calibration

The core innovation of LoCA is a two-stage process that decouples the credit assignment from the standard gradient descent flow. First, the model undergoes a one-shot calibration phase, which establishes a baseline for local error signals. Instead of propagating errors through the entire depth of the network, LoCA utilizes local credit assignment—a technique where individual layers or blocks are updated based on locally computed objectives. This allows for parallelized updates and significantly lower memory footprints, as each layer can be optimized independently without waiting for the full chain rule traversal across the entire model architecture.

Practical Implications for Model Tuning

By utilizing forward-only tuning, LoCA makes it feasible to perform fine-tuning on hardware that would otherwise be unable to handle the memory requirements of standard backpropagation. This approach is particularly relevant for adapting large models to specific downstream tasks where full-parameter updates are too expensive, but parameter-efficient fine-tuning (PEFT) methods might lack the necessary depth of adaptation. The local credit assignment ensures that the model maintains performance parity with traditional methods while drastically improving the efficiency of the training pipeline.