You Do Not Fully Utilize Transformer's Representation Capacity

Official implementation of the paper "You Do Not Fully Utilize Transformer's Representation Capacity".

About

This repository provides an implementation of Layer-Integrated Memory (LIMe), a lightweight Transformer extension that learns per-head, per-layer routing weights to fuse representations from all previous layers. LIMe mitigates representation collapse, boosts convergence and perplexity-per-FLOP, and delivers significant gains on language modeling, synthetic reasoning benchmarks, and very deep architectures-all with negligible overhead.

Installation

Clone the repository and install the required dependencies:

git clone https://github.com/corl-team/lime.git
cd lime
pip install -r requirements.txt
pip install -e .

Dataset Preparation

All the config classes are located in config.py. Before start, ensure that you set up data_path in DataConfig, path to the downloaded dataset.

Download and preprocess deduplicated FineWeb-Edu dataset.

python src/datasets/prepare_fineweb.py

Training

Use the following commands to start training:

export WANDB_API_KEY="YOUR_API_KEY"export WANDB_ENTITY="YOUR_WANB_ENTITY"
accelerate launch --mixed_precision "bf16" --multi_gpu train.py \
--config_path /app/configs/config_base.yaml --wandb_config.project "lime"

Also, you can add any specific arguments for config classes attributes. Navigate to config.py for more information.

LM Evaluation Harness benchmarks with 3-shot

Model	MultiRC	WiC	QNLI	ARC-E	ARC-C	KV	Induction	LD-3	Avg
LLaMA	43.24	50.00	49.49	70.45	38.70	45.94	54.20	33.60	48.20
HC	54.34	49.72	49.43	71.15	37.63	51.68	51.59	33.87	49.93
LIMe	56.15	50.44	51.43	71.15	39.30	55.64	55.36	34.47	51.74

Analysis

All the source code for analysis is stored in src/analysis/.

Citation

@article{gerasimov2025fullyutilizetransformersrepresentation,
title={You Do Not Fully Utilize Transformer's Representation Capacity}, author={Gleb Gerasimov and Yaroslav Aksenov and Nikita Balagansky and Viacheslav Sinii and Daniil Gavrilov},
year={2025},
eprint={2502.09245},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2502.09245}, }