Stuffed Mamba: State Collapse and State Capacity of RNN-Based Long-Context Modeling (2410.07145v1)

Abstract: One essential advantage of recurrent neural networks (RNNs) over transformer-based LLMs is their linear computational complexity concerning the sequence length, which makes them much faster in handling long sequences during inference. However, most publicly available RNNs (e.g., Mamba and RWKV) are trained on sequences with less than 10K tokens, and their effectiveness in longer contexts remains largely unsatisfying so far. In this paper, we study the cause of the inability to process long context for RNNs and suggest critical mitigations. We examine two practical concerns when applying state-of-the-art RNNs to long contexts: (1) the inability to extrapolate to inputs longer than the training length and (2) the upper bound of memory capacity. Addressing the first concern, we first investigate state collapse (SC), a phenomenon that causes severe performance degradation on sequence lengths not encountered during training. With controlled experiments, we attribute this to overfitting due to the recurrent state being overparameterized for the training length. For the second concern, we train a series of Mamba-2 models on long documents to empirically estimate the recurrent state capacity in LLMing and passkey retrieval. Then, three SC mitigation methods are proposed to improve Mamba-2's length generalizability, allowing the model to process more than 1M tokens without SC. We also find that the recurrent state capacity in passkey retrieval scales exponentially to the state size, and we empirically train a Mamba-2 370M with near-perfect passkey retrieval accuracy on 256K context length. This suggests a promising future for RNN-based long-context modeling.

• The paper reveals that state collapse significantly undermines RNNs' capacity to handle sequences longer than those encountered during training.
• It employs controlled experiments to identify exploding state channels and offers training-free techniques to mitigate performance degradation.
• Using extended pre-training on longer sequences, the approach achieves near-perfect passkey retrieval on contexts reaching 256K tokens.

Analysis of "Stuffed Mamba: State Collapse and State Capacity of RNN-Based Long-Context Modeling"

The paper "Stuffed Mamba: State Collapse and State Capacity of RNN-Based Long-Context Modeling" presents a comprehensive paper on the limitations and potential of Recurrent Neural Networks (RNNs) in processing long-context sequences effectively. The primary contribution of this research is the discovery and exploration of a phenomenon termed "state collapse" (SC) that impacts the length generalization capabilities of RNNs.

Key Insights

The authors identify and address two significant challenges associated with RNN-based models for long-context tasks: the inability to extrapolate beyond the training length and the finite capacity of contextual memory. Through controlled experiments, it is demonstrated that state collapse is a critical issue where certain recurrent states fail to generalize effectively, especially beyond the lengths encountered during training. This manifestation is primarily due to overparameterization, leading to severe degradation in performance as sequence length increases.

Methodology and Results

To identify the root cause of SC, the authors inspect state statistics and identify that a few dominant channels within the state's distribution exhibit exploding values. This explosion disrupts the normalization of output hidden representations, leading to SC. Notably, this behavior is observed across different prompts, further indicating its inherent nature rather than being data-dependent.

Several mitigation strategies are proposed:

1. Training-Free Methods: The paper introduces three techniques that modify the update rule of RNNs. These include:
   • Adjusting memory retention and insertion strength.
   • Implementing state normalization.
   • Reformulating the recurrent state into a sliding window mechanism.
2. Training on Longer Sequences: By leveraging a strategy of continual pre-training on extended sequences, the authors successfully alleviate SC, allowing RNNs to generalize over more than one million tokens without collapse.

Empirical evaluations reveal that these approaches significantly improve the length generalization without additional training, with Mamba-2 models achieving near-perfect passkey retrieval accuracy on context lengths reaching 256K tokens.

Implications

The insights from this paper have profound implications for AI research focused on enhancing RNN capabilities. The findings suggest that the prevailing training lengths used for RNN-based models may be inadequate, and more efficient training strategies could unlock significant performance improvements. The proposed methods not only address the state collapse but also elucidate a clear relationship between state capacity and model size, revealing that the state capacity scales exponentially for tasks like passkey retrieval.

Future Developments

The paper outlines a promising future for RNN-based models in long-context processing. The research opens avenues for further exploration into adaptive models that can dynamically adjust state parameters based on context length. Moreover, the insights into state overparameterization offer a foundation for developing more robust training protocols tailored to specific task requirements.

Conclusion

Overall, this paper provides a meticulous analysis of the limitations and potential of RNNs in long-context modeling, suggesting impactful methodologies to circumvent state collapse. These findings not only enhance our understanding of RNN-based models but also pave the way for their application to more computationally demanding tasks. The authors' rigorous approach in dissecting the phenomenon and proposing actionable solutions marks a significant contribution to the field of natural language processing.