Sequence classification is essential in NLP for understanding and categorizing language patterns in tasks like sentiment analysis, intent detection, and topic classification. Transformer-based models, despite achieving state-of-the-art performance, have inherent limitations due to quadratic time and memory complexity, restricting their input length. Although extensive efforts have aimed at reducing computational demands, processing extensive contexts remains challenging. To overcome these limitations, we propose ResFormer, a novel neural network architecture designed to model varying context lengths efficiently through a cascaded methodology. ResFormer integrates an reservoir computing network featuring a non-linear readout to effectively capture long-term contextual dependencies in linear time. Concurrently, short-term dependencies within sentences are modeled using a conventional Transformer architecture with fixed-length inputs. Experiments demonstrate that ResFormer significantly outperforms baseline models of DeepSeek-Qwen and ModernBERT, delivering an accuracy improvement of up to +22.3% on the EmoryNLP dataset and consistent gains on MultiWOZ, MELD, and IEMOCAP. In addition, EchoFormer exhibits reduced memory consumption, underscoring its effectiveness and efficiency in modeling extensive contextual information.

ResFormer: All-Time Reservoir Memory for Long Sequence Classification

Recent breakthroughs in natural language processing (NLP) have come with escalating model sizes and computational costs, posing significant challenges for deployment in real-time and resource-constrained environments. We introduce EmByte, a novel byte-level NLP model that achieves substantial compression while preserving accuracy and enhancing privacy. At the core of EmByte is a new Decompose-and-Compress (DeComp) learning strategy that decomposes subwords into fine-grained byte embeddings and then compresses them via neural projection. This enables EmByte to be shrinked down to any vocabulary size (e.g., 128 or 256), drastically reducing parameter count up to 94% compared to subword-based models without increasing sequence length or degrading performance. Unlike conventional tokenization-based and model-resizing approaches, EmByte is resilient to privacy threats such as gradient inversion attacks, due to its byte-level many-to-one mapping structure. Empirical results on seven NLP tasks demonstrate that EmByte matches or exceeds the accuracy of much larger models while improving efficiency, leading to lightweight and generalized NLP models suitable for deployment in privacy-sensitive and low-resource settings.

EmByte: Decomposition and Compression Learning for Small yet Private NLP

Supervised fine-tuning (SFT) is a widely used and highly effective method for adapting Large Language Models (LLMs) to specific tasks. However, it often suffers from overfitting, causing models to excel on fine-tuned data but struggle with unseen or rare real-world inputs. While recent methods like Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with AI Feedback (RLAIF) aim to align LLMs with human values and tasks, they face challenges such as the high cost of human labeling or instabilities and biases inherent in using LLMs as judges. To address these issues, we propose a novel approach called Reinforcement Learning from supervised Alignment (RLA), which constructs a supervised alignment to train the reward model for reinforcement learning. Using only 100,000 MS MARCO samples, our method outperforms RLAIF by a relative margin ranging from +5.38% to +131.8%. It also significantly enhances the baseline Llama3 LLM, achieving up to +55% improvement on in-domain tasks and up to +16% on out-of-domain tasks. While RLA slightly underperforms supervised fine-tuning (SFT) on in-domain benchmarks, it surpasses SFT by up to 50 times on out-of-domain and cross-task evaluations, demonstrating strong generalization capabilities.

Reinforcement Learning with Supervised Alignment

We introduce our probabilistic robustness rewarded data optimization (PRoDO) approach as a framework to enhance the model's generalization power by selecting training data that optimizes our probabilistic robustness metrics. We use proximal policy optimization (PPO) reinforcement learning to approximately solve the computationally intractable training subset selection problem. 
The PPO's reward is defined as our (\$\textbackslash{}alpha,\textbackslash{}epsilon, \textbackslash{}gamma\$)-Robustness that measures performance consistency over multiple domains by simulating unknown test sets in real-world scenarios using a leaving-one-out strategy. We demonstrate that our PRoDO effectively filters data that lead to significantly higher prediction accuracy and robustness on unknown-domain test sets. Our experiments achieve up to +17.2\textbackslash{}\% increase of accuracy (+25.5\textbackslash{}\% relatively) in sentiment analysis, and -28.05 decrease of perplexity (-32.1\textbackslash{}\% relatively) in language modeling.
In addition, our probabilistic (\$\textbackslash{}alpha,\textbackslash{}epsilon, \textbackslash{}gamma\$)-Robustness definition serves as an evaluation metric with higher levels of agreement with human annotations than typical performance-based metrics.

Jia Xu

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