Title: Understanding Differential Transformer Unchains Pretrained Self-Attentions
Venue: NeurIPS 2025
Accepted: yes
Keywords: Attention

Transformer

Language Models
Abstract: Differential Transformer has recently gained significant attention for its impressive empirical performance, often attributed to its ability to perform noise canceled attention. However, precisely how differential attention achieves its empirical benefits remains poorly understood. Moreover, Differential Transformer architecture demands large-scale training from scratch, hindering utilization of open pretrained weights. In this work, we conduct an in-depth investigation of Differential Transformer, uncovering three key factors behind its success: (1) enhanced expressivity via negative attention, (2) reduced redundancy among attention heads, and (3) improved learning dynamics. Based on these findings, we propose DEX, a novel method to efficiently integrate the advantages of differential attention into pretrained language models. By reusing the softmax attention scores and adding a lightweight differential operation on the output value matrix, DEX effectively incorporates the key advantages of differential attention while remaining lightweight in both training and inference. Evaluations confirm that DEX substantially improves the pretrained LLMs across diverse benchmarks, achieving significant performance gains with minimal adaptation data (< 0.01%).


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents two main contributions. Firstly, it provides an investigation into Differential Transformers, including their internal workings, the phenomenon of Negative Attention, reductions in attention head redundancy, and their training dynamics. Secondly, the authors introduce DEX, a transformer architecture that, while conceptually related to Differential Transformers, is still different. DEX offers a one advantage over Differential Transformers: its applicability to pretrained models. Through extensive experimentation on various pretrained transformer models, the authors demonstrate DEX's superior performance across multiple tasks, outperforming LoRA, full fine-tuning, and other common fine-tuning strategies. The paper is well-supported by a thorough ablation study and offers valuable initial insights into the characteristics of models fine-tuned with DEX.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nW1: The paper provides detailed investigation of Differential Transformers, illustrating the components learned by this architectural design. This contributes to understanding the inner workings of this architecture.\n\nW2: The proposed DEX architecture is novel and appears to outperform existing fine-tuning approaches.\n\nW3: The ablation study is both important and well-executed.\n\nW4: The paper is clear and well-structured.\n\nWeaknesses\n\nS1: The main weakness here is that the DEX architecture is actually different from Differential Transformers, and it's not truly clear that DEX performs as Differential Transformers do. I think you should first compare Differential Transformer and DEX when trained from scratch, as was done for Section 2 when comparing to LLaMA. Then, assuming DEX and Differential Transformers behave similarly, you can apply DEX to pretrained models. Training DEX from scratch, mirroring how LLaMA and Differential Transformers were handled for Section 2, would be very beneficial.\n\nS2: A more minor weakness is that throughout the paper, all the legends in the figures are very small and very hard to read. The authors should fix this for improved clarity.\n\nQUESTIONS:\nQ1: Regarding Figure 8a, the trend seems to indicate that training for more steps benefits LLaMA more than the Differential Transformer, potentially leading to a better final loss for LLaMA. Could the authors train for a longer duration and report these extended results to explore whether a regular Transformer architecture might ultimately be superior?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper analyzes why Differential Transformers (DIFF) outperform standard transformers and proposes DEX, a lightweight, efficient extension for infusing differential attention's benefits into existing pretrained models without the need for training from scratch. The core claims are that DIFF attention enables negative attention scores (higher expressivity), reduces attention head redundancy, and improves learning dynamics. DEX is evaluated on various benchmarks, demonstrating improved performance with minimal data and computational overhead\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Provide detailed insights to the effective of Differential Transformers.\n- Propose DEX - a novel lightweight alternative for DIFFT, which can leverage minimal data and computational overhead.  \n- Experiments are conducted on diverse benchmarks, showcasing the benefits of DEX.\n\nWeaknesses:\n- My main concerns lie on the experimental setup. Current experiments use a **custom-mixed dataset**, not the original pretraining data for models like Llama or Qwen. Thus, the absolute improvement and generalization to other training regimes or more realistic finetuning settings are somewhat unclear. Besides, **direct comparisons to original DIFF Transformer are limited**. The authors should provide the performance of DIFF on the main language modeling benchmark (different models and scope), and other tasks (Key IR, In-Context Learning). \n\n- **Ambiguity in Head Selection Strategy**: The paper proposes two head selection strategies—low importance and high entropy—but lacks detailed comparative results clearly indicating why one strategy is preferable. Additionally, practical guidelines or robustness analyses concerning the selection of heads across different tasks and models would improve usability.\n\n- **Limited Theoretical Justification**: The paper utilizes empirical observations and visualizations (e.g., attention entropy, redundancy) to interpret the Differential Transformers. However, it provides limited theoretical insights to justify why the differential attention approach outperforms standard attention mechanisms beyond empirical observations. A deeper theoretical exploration or formal analysis would significantly enhance understanding.\n\n- **Potentially Overstated Generalization Claims:** The claim of significant gains using minimal adaptation data (<0.01%) is strong but could be overstated. Further analysis is needed regarding whether such minimal training consistently delivers strong performance across a broader range of datasets and scenarios.\n\nQUESTIONS:\nPlease see weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nDifferential transformers have recently established itself as an architecture with competitive or better performance compared to traditional transformers. This paper provides an in-depth analysis of the benefits and working mechanism of differential transformers. By showing the main benefits of differential transformers, namely 1) negative attention scores potentially provides additional expressivity, 2) it allows focusing mainly on salient tokens, 3) the additional lambda parameter helps smoother learning dynamics. \n\nThe paper then goes on to provide a method called DEX that introduces the benefits of differential transformers directly on top of pretrained transformers. Dex uses a learnable scalar lambda and additional learnable projection matrix for a fraction of all attention weights in the pretrained transformer. This allows finetuning of pretrained transformers to get benefits of differential transformers.\n\nExperiments on Llama-3.1-8B, Llama-3.2-3B/1B and Qwen-2.5-1.5B/0.5B finetuned on dataset created by the authors show clear benefit of DEX. Further experiments on key information retrieval and in-context learning shows the benefit of DEX.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The paper provides an in-depth analysis of the advantages of differential transformer compared to a standard transformer, that is 1) more expressive attention weights since attention can be negative, 2) selective attention heads for salient tokens, 3) an additional lambda parameter that helps with the learning dynamics.\n\n- The paper provides a method to obtain the benefits of a differential transformer on top of standard transformer by finetuning pretrained models. This allows preserving the pretrained knowledge as well as gain the benefits of differential transformers\n\n- The paper also creates a dataset for finetuning that shows good results on the experiments\n\n- Experimental results show improvement compared to standard finetuning of transformers\n\nWeaknesses\n- The experiments are restricted to older datasets. The models considered finetuned have been extensively evaluated on many recent datasets such as MATH500, GSM8k, MBPP, HumaEval, AIME (maybe AIME is not recommended for the selected models in the paper). Can the authors show performance on these datasets as well? The authors can also choose to finetune or apply their method on NuminaMath and evaluate on MATH500 or GSM8k (for non-overlapping cases between NuminaMath and Math500).\n\nQUESTIONS:\nHow important is the head selection, what happens if we do not use head selection in Tab. 4 and try out different combination of lambda_learned and lambda_annealing?\n\nPlease also check the weaknesses if it is possible to show experiments on other datasets\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces DEX (Differential Extension), an extension designed for the recently proposed Differential Transformer (DIFFT) framework.  The paper begins by explaining why DIFFT works in practice, empirically demonstrating that DIFFT takes advantage of: a) discrepancies between all attention (vs salient) scores, (b) negative relevance in QK values, (c) reduced redundancy in attention heads, and (d) improved learning dynamics.  DEX is then introduced, the chief innovation of which is moving trainable parameters from a second set of QK matrices (i.e., DIFFT) to the regular output matrix (OV) minus a \\lambda* parameterized-weight matrix *OV.  DEX also incorporates adaptive head selection (low-importance and high-entropy) during differential adaptation and an annealing schedule to \\lambda.  DEX performance is subsequently demonstrated over Llama3 and Qwen2.5 models of various sizes against various PEFT methods (adapted over the base model) for common reasoning benchmarks.  While improvements are not always substantial, DEX does consistently improve performance across all models, which is not the case for competitor methods (including full fine-tuning).  Additional experiments key information retrieval and ICL also demonstrate the utility of the proposed method relative to full fine-tuning/LoRA/base model performance.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n## Signifiance and Originality\nThe DIFFT framework has been a high impact contribution since its debut.  The fundamental idea behind DEX, i.e., removing dual QK matrices in favor of a single parameterized weight on the output-value matrix, is a very interesting and intuitive approach.\n\n## Quality\nMany of the experiments in Sections 2 and 4 were high quality, supporting the several points made in the text.\n\n# Weaknesses\n## Quality\nA critical issue is the lack of necessary experiments/benchmarks.  E.g.:\n> Direct comparison with original DIFF Transformer is limited by unavailable pretrained weights, which\n we defer to Appendix along with other details.\n\nComparison to DIFFT is necessary to show demonstrate the effectiveness of DEX relative to the work it builds on.  Even if the checkpoint is unavailable,\n the code from the Differential Transformer paper is available:\nhttps://github.com/microsoft/unilm/tree/master/Diff-Transformer\n\nFurthermore, given the custom data used for the experiments in Table 1, comparing to the DIFFT checkpoint would not be a fair comparison; to ensure a fair comparison, DIFFT would have to be trained on the same data and training recipe as used for DEX.\n\nAdditionally, one of the major claimed drawbacks to DIFFT is the following:\n> We note that despite DIFF attention having the same number of parameters, it exhibits significantly higher compute cost and peak memory usage in practice due to enlarged dimensions\n\nThis is a main criticism of DIFFT, and motivation for DEX.  Thus, a heads up comparison showing both training/testing runtime and memory usage between DIFFT and DEX is required.\n\nFinally, some presented experiments do not accurately support the claims being made.  E.g.:\n\n\"Training statistics further corroborate this finding. Fig.8 plots the language modeling loss and gradient\nnorms for the standard and DIFF transformer. While DIFF consistently achieves lower loss and more\nstable grad norms, removing the learnable λ notably impairs optimization. We hypothesize that the\nlearnable λ plays a key role in stabilizing training dynamics, especially during the early stages.\" <- At the end of the day, these do not equate to downstream performance.  Could the authors test the model (is this Llama3?  Which one?) w and w/o learnable \\lambdas on natural language tasks to show that there is some performative difference?\n\nIn Section 3.3, zero-initializing \\lambda is the not the only option.  It is unclear whether annealing is required, or some randomly (perhaps Gaussian) sampled values would be sufficient.  Furthermore, in the ablation experiments:\n> However, adopting the initialization scheme from the original DIFF Transformer setting yields slightly the best\nperformance.\n\nThus, it is thus unclear whether \\lambda annealing is thus a valid design choice.  Based on these results, \\lambda annealing is suboptimal and would ideally be replaced by the DIFFT initialization scheme in the main results.\n\n## Clarity\nThe paper has substantial room to improve in clarity.  For instance, important details are lacking when describing the experiments.  These critically limit the ability to understand these experiments.  \n- For Figure 1, can the authors please detail the experiments either in the figure caption or inline (i.e., lines 70-79).  Reading through those lines, it is not clear what dataset or models are being presented.\n- For Figure 2, could the authors please include what LLMs are being evaluated, e.g., is it Llama-3.2-1B?\n- \"Notably, this benefit is largely lost when the learnable λ is removed\" <- What does this mean?  Is \\lambda set to one in this case?\n- Need similar model/data details for the following experiments: \"Qualitative examples in Fig.4, such as down-weighting irrelevant subject in Indirect Object Identification task [77] or non-literal interpretation in sarcasm detection, illustrate how DIFF attention can achieve a more refined information flow using negative attention (green boxes).\"\n- Can the algorithms for lines 173-175 and 176-179 be written out for full clarity?\n- \"Figure 5\" <- Please include a colorbar.  Also suggest adding a down arrow to (a) and an up arrow to (b).\n\nThe writing also has significant room for improvement.  Currently, the paper feels crammed, which may be why many details are lacking for the experiments in Section 2.  Some of the text could be tightened up/condensed, e.g., lines 145-151 and 152-158, as well as lines 161-164 (which would ideally go in the discussion section).\n\nQUESTIONS:\n# Minor suggestions\nFor Figure 1, could the authors add up arrows to subfigures (a) and (b) and downarrows for subfigures (c) and (d) (as well as mention these trends for the respective metrics))?  This will help the reader quickly parse the results (especially since the similarity trends in (a-b) are flipped in (c-d)) to see the stated conclusion: \" It clearly shows that the overlap between the two attention scores is much greater in non-salient tokens.\"  Also, listing curve averages and contrasting \"Top 5%\" vs \"All\" will help quantify the stated conclusion.\n\n# Questions\n- \"Although DEX is not presented as a fine-tuning method, we compare against baselines trained on the same data\" <- Is DEX trained using full precision then?\n- Is there a downside to Equation 3?  It seems like there is a tradeoff between granularity for efficiency that could be mentioned.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}