Leveraging the true depth of LLMs

• URL: http://arxiv.org/abs/2502.02790v2
• Date: Sat, 17 May 2025 07:29:57 GMT
• Title: Leveraging the true depth of LLMs
• Authors: Ramón Calvo González, Daniele Paliotta, Matteo Pagliardini, Martin Jaggi, François Fleuret,
• Abstract summary: Large Language Models (LLMs) demonstrate remarkable capabilities at the cost of high compute requirements.<n>Recent studies have demonstrated that intermediate layers in LLMs can be removed or reordered without substantial accuracy loss.<n>We propose a novel method that groups consecutive layers into pairs evaluated in parallel.
• Score: 46.81174316936993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) demonstrate remarkable capabilities at the cost of high compute requirements. Recent studies have demonstrated that intermediate layers in LLMs can be removed or reordered without substantial accuracy loss; however, this insight has not yet been exploited to improve inference efficiency. Leveraging observed layer independence, we propose a novel method that groups consecutive layers into pairs evaluated in parallel, effectively restructuring the computational graph to enhance parallelism. Without requiring retraining or fine-tuning, this approach achieves an inference throughput improvement of 1.05x-1.20x on standard benchmarks, retaining 95\%-99\% of the original model accuracy. Empirical results demonstrate the practicality of this method in significantly reducing inference cost for large-scale LLM deployment. Additionally, we demonstrate that modest performance degradation can be substantially mitigated through lightweight fine-tuning, further enhancing the method's applicability.

Related papers

• SDMPrune: Self-Distillation MLP Pruning for Efficient Large Language Models [3.962074007736394]
  We introduce a self-distillation loss during the pruning phase (rather than post-training) to fully exploit the predictions of the original model.<n>We demonstrate that our method significantly outperforms existing pruning methods.<n>Our method achieves very competitive performance among 1B-scale open source LLMs.
  arXiv  Detail & Related papers  (2025-06-10T02:24:32Z)
• Supervised Optimism Correction: Be Confident When LLMs Are Sure [91.7459076316849]
  We establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning.<n>We show that the widely used beam search method suffers from unacceptable over-optimism.<n>We propose Supervised Optimism Correction, which introduces a simple yet effective auxiliary loss for token-level $Q$-value estimations.
  arXiv  Detail & Related papers  (2025-04-10T07:50:03Z)
• Seesaw: High-throughput LLM Inference via Model Re-sharding [8.840996987380484]
  We present Seesaw, an inference engine optimized for throughput-oriented tasks. Key idea behind Seesaw is dynamic model re-sharding, a technique that facilitates the dynamic reconfiguration of parallelization strategies.
  arXiv  Detail & Related papers  (2025-03-09T04:14:06Z)
• Determining Layer-wise Sparsity for Large Language Models Through a Theoretical Perspective [55.90119819642064]
  We address the challenge of determining the layer-wise sparsity rates of large language models (LLMs) through a theoretical perspective.<n>This refers to the cumulative effect of reconstruction errors throughout the sparsification process.<n>We derive a simple yet effective approach to layer-wise sparsity allocation that mitigates this issue.
  arXiv  Detail & Related papers  (2025-02-20T17:51:10Z)
• Dynamic Noise Preference Optimization for LLM Self-Improvement via Synthetic Data [51.62162460809116]
  We introduce Dynamic Noise Preference Optimization (DNPO) to ensure consistent improvements across iterations. In experiments with Zephyr-7B, DNPO consistently outperforms existing methods, showing an average performance boost of 2.6%. DNPO shows a significant improvement in model-generated data quality, with a 29.4% win-loss rate gap compared to the baseline in GPT-4 evaluations.
  arXiv  Detail & Related papers  (2025-02-08T01:20:09Z)
• The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws [51.608402959163925]
  We present the first systematic exploration of optimal sparse pre-training configurations for large language models.<n>We find that initiating pruning at 25% of total training compute and concluding at 75% achieves near-optimal final evaluation loss.<n>We propose a new scaling law that modifies the Chinchilla scaling law to use the average parameter count over pre-training.
  arXiv  Detail & Related papers  (2025-01-21T20:23:22Z)
• Efficient Diffusion as Low Light Enhancer [63.789138528062225]
  Reflectance-Aware Trajectory Refinement (RATR) is a simple yet effective module to refine the teacher trajectory using the reflectance component of images. textbfReflectance-aware textbfDiffusion with textbfDistilled textbfTrajectory (textbfReDDiT) is an efficient and flexible distillation framework tailored for Low-Light Image Enhancement (LLIE)
  arXiv  Detail & Related papers  (2024-10-16T08:07:18Z)
• Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification [76.14641982122696]
  We propose a constraint learning schema for fine-tuning Large Language Models (LLMs) with attribute control. We show that our approach leads to an LLM that produces fewer inappropriate responses while achieving competitive performance on benchmarks and a toxicity detection task.
  arXiv  Detail & Related papers  (2024-10-07T23:38:58Z)
• On Effects of Steering Latent Representation for Large Language Model Unlearning [4.058064008234271]
  Representation Misdirection for Unlearning (RMU) is an effective method for large language model (LLM) unlearning.<n>We show that steering forget representations in the intermediate layer reduces token confidence, causing LLMs to generate wrong or nonsense responses.<n>We propose Adaptive RMU -- a simple yet effective alternative method that makes unlearning effective with most layers.
  arXiv  Detail & Related papers  (2024-08-12T15:24:50Z)
• Pruning Large Language Models with Semi-Structural Adaptive Sparse Training [17.381160429641316]
  Adaptive Sparse Trainer (AST) is a novel and efficient retraining framework tailored for semi-structured sparse models.<n>AST reduces the perplexity and zero-shot accuracy gap between dense and 2:4 semi-structured sparse models to 0.6 and 1.16%, respectively.
  arXiv  Detail & Related papers  (2024-07-30T06:33:44Z)
• A deeper look at depth pruning of LLMs [49.30061112976263]
  Large Language Models (LLMs) are resource-intensive to train but more costly to deploy in production. Recent work has attempted to prune blocks of LLMs based on cheap proxies for estimating block importance. We show that adaptive metrics exhibit a trade-off in performance between tasks.
  arXiv  Detail & Related papers  (2024-07-23T08:40:27Z)
• Accelerating LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with LITE [62.13435256279566]
  Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks. However, their large size makes their inference slow and computationally expensive. We show that it enables these layers to acquire 'good' generation ability without affecting the generation ability of the final layer.
  arXiv  Detail & Related papers  (2023-10-28T04:07:58Z)
• Towards Compute-Optimal Transfer Learning [82.88829463290041]
  We argue that zero-shot structured pruning of pretrained models allows them to increase compute efficiency with minimal reduction in performance. Our results show that pruning convolutional filters of pretrained models can lead to more than 20% performance improvement in low computational regimes.
  arXiv  Detail & Related papers  (2023-04-25T21:49:09Z)
• Rényi Divergence Deep Mutual Learning [3.682680183777648]
  This paper revisits Deep Learning Mutual (DML) as a simple yet effective computing paradigm. We propose using R'enyi divergence instead of the KL divergence, which is more flexible and limited. Our empirical results demonstrate the advantage combining DML and R'enyi divergence, leading to further improvement in model generalization.
  arXiv  Detail & Related papers  (2022-09-13T04:58:35Z)
• Extrapolation for Large-batch Training in Deep Learning [72.61259487233214]
  We show that a host of variations can be covered in a unified framework that we propose. We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer.
  arXiv  Detail & Related papers  (2020-06-10T08:22:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.