OpenNovelty.org

Arbitrary-Order Block SignSGD for Memory-Efficient LLM Fine-Tuning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Block-Coordinate OptimizationSignSGDLarge Language Models (LLMs)Memory-Efficient Fine-Tuning
Abstract:

We propose \textbf{ABSignSGD}, a block‑coordinate variant of sign-based descent with flexible block selection that enables memory‑ and runtime‑efficient full‑parameter fine‑tuning of large language models. We present a unified convergence analysis under mild conditions, covering both the base method and a \textit{majority‑vote} extension for distributed training. The latter improves communication efficiency by aggregating only gradient signs rather than averaging full gradients. Experiments on \textcolor{blue}{Qwen3‑8B, Llama3-8B, and Qwen3-32B}, spanning mathematical reasoning and general instruction‑following tasks, show that ABSignSGD converges faster per iteration and delivers superior downstream performance while reducing both runtime and memory usage compared to existing methods. Ablation studies further indicate that the memoryless sign-based update naturally complements block‑wise updates, explaining the method’s strong empirical performance.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces ABSignSGD, a block-coordinate variant of sign-based descent with flexible block selection for memory-efficient full-parameter fine-tuning. It resides in the Block Coordinate Descent Variants leaf, which contains three papers including the original work. This leaf sits within First-Order Optimizer Modifications under Optimizer-Based Memory Reduction, representing a focused but not overcrowded research direction. The taxonomy shows that block coordinate methods form one of several parallel approaches to optimizer state reduction, alongside gradient subspace projection and fused gradient computation.

The Block Coordinate Descent Variants leaf neighbors Gradient Subspace Projection Techniques (four papers) and Fused Gradient Computation (one paper), both addressing optimizer memory through different mechanisms. The broader Optimizer-Based Memory Reduction branch contrasts with Activation and Backward Pass Optimization and Quantization-Aware Full-Parameter Training, which target different memory bottlenecks. The taxonomy's scope notes clarify that block coordinate methods partition parameters for iterative updates, excluding gradient projection approaches that operate in lower-dimensional subspaces. This positioning suggests ABSignSGD extends an established paradigm rather than opening an entirely new direction.

Among the three contributions analyzed, the unified convergence analysis shows the most substantial prior work overlap: nine candidates examined, three appearing refutable based on the limited search. The core ABSignSGD algorithm and depth-biased update strategy show less overlap, with one and seven candidates examined respectively, none clearly refuting either contribution. The analysis explicitly notes examination of seventeen total candidates from top-K semantic search plus citation expansion, not an exhaustive literature review. This limited scope means the refutability signals reflect only the most semantically similar work retrieved, not the entire field.

Given the seventeen-candidate search scope, the analysis suggests moderate novelty within a defined research niche. The block coordinate descent leaf's three-paper population indicates active but not saturated exploration. The convergence analysis contribution faces more substantial prior work among examined candidates, while the algorithmic and scheduling contributions appear less directly anticipated. The taxonomy structure reveals ABSignSGD as an incremental advance within optimizer-based memory reduction, combining established block-wise and sign-based techniques in a new configuration.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
17
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: memory-efficient full-parameter fine-tuning of large language models. The field addresses the challenge of adapting billion-parameter models under constrained memory budgets, and the taxonomy reflects a diverse set of strategies. Optimizer-Based Memory Reduction focuses on modifying or replacing standard optimizers (e.g., Adam) with variants that reduce state overhead, including block coordinate descent approaches like BAdam[2] and Blockllm[47]. Activation and Backward Pass Optimization targets intermediate tensors through techniques such as gradient checkpointing and selective layer updates. Quantization-Aware Full-Parameter Training applies low-bit representations during training to compress both weights and optimizer states. Parameter-Efficient Adaptation Techniques, while not strictly full-parameter, explore low-rank and sparse updates that approximate full fine-tuning with fewer trainable elements. System-Level and Distributed Training Optimizations leverage offloading, pipeline parallelism, and memory-aware scheduling, while Frameworks, Benchmarks, and Empirical Studies provide tooling and comparative analyses. Domain-Specific and Application-Oriented Fine-Tuning examines specialized use cases where memory constraints are particularly acute. Within Optimizer-Based Memory Reduction, block coordinate descent variants have emerged as a particularly active line of work, updating only subsets of parameters per iteration to limit optimizer state footprint. Block SignSGD[0] exemplifies this direction by combining block-wise updates with sign-based gradient compression, aiming to balance convergence quality and memory savings. This contrasts with BAdam[2], which partitions parameters into blocks and applies adaptive learning rates selectively, and Blockllm[47], which explores block-level scheduling strategies. A key trade-off across these methods is the granularity of blocking: finer partitions can improve convergence but may increase coordination overhead, while coarser blocks simplify implementation at the cost of slower adaptation. Open questions include how to dynamically select block sizes, whether block-wise schemes generalize across model architectures, and how they interact with quantization or offloading. Block SignSGD[0] sits naturally among these block coordinate descent variants, emphasizing sign-based compression as an additional memory lever compared to the adaptive blocking in BAdam[2] or the scheduling focus of Blockllm[47].

Claimed Contributions

ABSignSGD: Block-coordinate SignSGD with arbitrary-order block selection

The authors propose ABSignSGD, a memory- and runtime-efficient optimizer that combines sign-based gradient descent with flexible block-coordinate updates. This design allows customized update strategies (such as depth-biased selection) that reduce both memory footprint and computational cost while maintaining competitive convergence and downstream performance.

1 retrieved paper
Unified convergence analysis for ABSignSGD and ABSignSGD-MV

The authors provide a unified theoretical framework proving O(1/√K) convergence rates for both the single-agent ABSignSGD and its distributed majority-vote variant (ABSignSGD-MV) under bounded update intervals and sign-agreement probability conditions. This analysis covers arbitrary block selection schemes within a common proof structure.

9 retrieved papers
Can Refute
Depth-biased update strategy for runtime speedup

The authors develop an event-driven depth-biased block selection rule that updates deeper network layers more frequently than shallower ones. This strategy exploits the structure of neural networks to reduce backpropagation costs, achieving additional runtime improvements beyond standard block-coordinate methods while maintaining strong empirical performance.

7 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

ABSignSGD: Block-coordinate SignSGD with arbitrary-order block selection

The authors propose ABSignSGD, a memory- and runtime-efficient optimizer that combines sign-based gradient descent with flexible block-coordinate updates. This design allows customized update strategies (such as depth-biased selection) that reduce both memory footprint and computational cost while maintaining competitive convergence and downstream performance.

Contribution

Unified convergence analysis for ABSignSGD and ABSignSGD-MV

The authors provide a unified theoretical framework proving O(1/√K) convergence rates for both the single-agent ABSignSGD and its distributed majority-vote variant (ABSignSGD-MV) under bounded update intervals and sign-agreement probability conditions. This analysis covers arbitrary block selection schemes within a common proof structure.

Contribution

Depth-biased update strategy for runtime speedup

The authors develop an event-driven depth-biased block selection rule that updates deeper network layers more frequently than shallower ones. This strategy exploits the structure of neural networks to reduce backpropagation costs, achieving additional runtime improvements beyond standard block-coordinate methods while maintaining strong empirical performance.

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