Planning vs Reasoning: Ablations to Test Capabilities of LoRA layers

• URL: http://arxiv.org/abs/2412.00029v2
• Date: Wed, 05 Feb 2025 10:01:29 GMT
• Title: Planning vs Reasoning: Ablations to Test Capabilities of LoRA layers
• Authors: Neel Redkar,
• Abstract summary: Low-Rank Adaptation layers have emerged as a promising approach for efficient model fine-tuning. This paper investigates the question of whether LoRA layers are effective at increasing reasoning + planning abilities.
• Score: 0.0
• License:
• Abstract: Low-Rank Adaptation (LoRA) layers have emerged as a promising approach for efficient model fine-tuning, but their capabilities and limitations have not been fully explored. This paper: 1) Investigates the fundamental question of whether LoRA layers are effective at increasing reasoning + planning abilities 2) We introduce HashChain Reasoning, a novel evaluation dataset that deterministically tests reasoning capabilities. Through systematic ablation studies on GPT-2, we demonstrate that reasoning capabilities appear to exist primarily in low-rank spaces and can be effectively enhanced using LoRA layers. The effective rank analysis of trained LoRA matrices reveals a 2-3x lower rank requirement for reasoning tasks compared to planning tasks, giving context on where LoRA layers would be effective. This also provides evidence for reasoning fundamentally preferring low-parameter spaces for generalization.

Related papers

• BeamLoRA: Beam-Constraint Low-Rank Adaptation [51.52097743781401]
  Low-Rank Adaptation (LoRA) has been widely adopted as one of the most effective parameter-efficient fine-tuning methods. We propose BeamLoRA, which conceptualizes each LoRA module as a beam where each rank naturally corresponds to a potential sub-solution.
  arXiv  Detail & Related papers  (2025-02-19T10:33:22Z)
• HRP: High-Rank Preheating for Superior LoRA Initialization [58.3319586613105]
  High-Rank Preheating (HRP) is proposed to fine-tune Low-Rank Adaptation (LoRA) HRP significantly enhances LoRA's generalization effectiveness across various models and tasks.
  arXiv  Detail & Related papers  (2025-02-11T17:59:35Z)
• S-LoRA: Scalable Low-Rank Adaptation for Class Incremental Learning [73.93639228235622]
  Continual Learning with foundation models has emerged as a promising approach to harnessing the power of pre-trained models for sequential tasks. We propose a Scalable Low-Rank Adaptation (S-LoRA) method for CL (in particular class incremental learning), which incrementally decouples the learning of the direction and magnitude of LoRA parameters. Our theoretical and empirical analysis demonstrates that S-LoRA tends to follow a low-loss trajectory that converges to an overlapped low-loss region, resulting in an excellent stability-plasticity trade-off in CL.
  arXiv  Detail & Related papers  (2025-01-22T20:00:41Z)
• AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality [31.830108790753172]
  Low-Rank Adaptation (LoRA) is known to enhance training efficiency in Large Language Models (LLMs) Recent studies seek to combine LoRA with Mixture-of-Experts (MoE) to boost performance across various tasks. We introduce AlphaLoRA, a theoretically principled and training-free method for allocating LoRA experts to further redundancy.
  arXiv  Detail & Related papers  (2024-10-14T00:43:02Z)
• LoRA-Pro: Are Low-Rank Adapters Properly Optimized? [121.0693322732454]
  Low-rank adaptation, also known as LoRA, has emerged as a prominent method for parameter-efficient fine-tuning of foundation models. Despite its computational efficiency, LoRA still yields inferior performance compared to full fine-tuning. We introduce LoRA-Pro, a method that enhances LoRA's performance by strategically adjusting the gradients of low-rank matrices.
  arXiv  Detail & Related papers  (2024-07-25T17:57:12Z)
• A Survey on LoRA of Large Language Models [19.85250609150331]
  Low-Rank Adaptation(LoRA), which updates the dense neural network layers with pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning paradigms. This survey categorizes and reviews the progress from the perspectives of (1) downstream adaptation improving variants that improve LoRA's performance on downstream tasks; (2) cross-task generalization methods that mix multiple LoRA plugins to achieve cross-task generalization; (3) efficiency-improving methods that boost the computation-efficiency of LoRA; (4) data privacy-preserving methods that use LoRA in federated learning; (5) application.
  arXiv  Detail & Related papers  (2024-07-08T12:32:10Z)
• ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models [8.251547772610301]
  We extend the methodology of low-rank adaptation (LoRA) to an innovative approach we call allocating low-rank adaptation (ALoRA) First, we propose a novel method, AB-LoRA, that can effectively estimate the importance score of each LoRA rank. Second, guided by AB-LoRA, we gradually prune abundant and negatively impacting LoRA ranks and allocate the pruned LoRA budgets to important Transformer modules needing higher ranks.
  arXiv  Detail & Related papers  (2024-03-24T15:09:55Z)
• LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation [27.123271324468657]
  Low-Rank Adaptation (LoRA) is currently the most commonly used. efficient fine-tuning (PEFT) method. It introduces auxiliary parameters for each layer to fine-tune the pre-trained model under limited computing resources. However, it still faces resource consumption challenges when scaling up to larger models.
  arXiv  Detail & Related papers  (2024-02-12T15:34:56Z)
• PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation [65.268245109828]
  We introduce PRILoRA, which linearly allocates a different rank for each layer, in an increasing manner, and performs pruning throughout the training process. We validate the effectiveness of PRILoRA through extensive experiments on eight GLUE benchmarks, setting a new state of the art.
  arXiv  Detail & Related papers  (2024-01-20T20:25:17Z)
• Sparse Low-rank Adaptation of Pre-trained Language Models [79.74094517030035]
  We introduce sparse low-rank adaptation (SoRA) that enables dynamic adjustments to the intrinsic rank during the adaptation process. Our approach strengthens the representation power of LoRA by initializing it with a higher rank, while efficiently taming a temporarily increased number of parameters. Our experimental results demonstrate that SoRA can outperform other baselines even with 70% retained parameters and 70% training time.
  arXiv  Detail & Related papers  (2023-11-20T11:56:25Z)

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.