Escaping Mode Collapse in LLM Generation via Geometric Regulation

Xin Du, Kumiko Tanaka-Ishii
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:26696-26714, 2026.

Abstract

Mode collapse is a persistent challenge in generative modeling and manifests in autoregressive text generation as behaviors ranging from explicit looping to gradual loss of diversity and premature trajectory convergence. We take a dynamical-systems view and reinterpret mode collapse as reduced state-space accessibility caused by geometric collapse: during generation, the model’s internal trajectory becomes confined to a low-dimensional region of its representation space. This implies mode collapse is not purely a token-level phenomenon and cannot be reliably mitigated by symbolic constraints or probability-only decoding heuristics. Guided by this perspective, we propose Reinforced Mode Regulation (RMR), a lightweight, online state-space intervention that regulates dominant self-reinforcing directions in the Transformer value cache (implemented as low-rank damping). Across multiple large language models, RMR substantially reduces mode collapse and enables stable, high-quality generation at extremely low entropy rates (down to 0.8 nats/step), whereas standard decoding typically collapses near 2.0 nats/step.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-du26m, title = {Escaping Mode Collapse in {LLM} Generation via Geometric Regulation}, author = {Du, Xin and Tanaka-Ishii, Kumiko}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {26696--26714}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/du26m/du26m.pdf}, url = {https://proceedings.mlr.press/v306/du26m.html}, abstract = {Mode collapse is a persistent challenge in generative modeling and manifests in autoregressive text generation as behaviors ranging from explicit looping to gradual loss of diversity and premature trajectory convergence. We take a dynamical-systems view and reinterpret mode collapse as reduced state-space accessibility caused by geometric collapse: during generation, the model’s internal trajectory becomes confined to a low-dimensional region of its representation space. This implies mode collapse is not purely a token-level phenomenon and cannot be reliably mitigated by symbolic constraints or probability-only decoding heuristics. Guided by this perspective, we propose Reinforced Mode Regulation (RMR), a lightweight, online state-space intervention that regulates dominant self-reinforcing directions in the Transformer value cache (implemented as low-rank damping). Across multiple large language models, RMR substantially reduces mode collapse and enables stable, high-quality generation at extremely low entropy rates (down to 0.8 nats/step), whereas standard decoding typically collapses near 2.0 nats/step.} }
Endnote
%0 Conference Paper %T Escaping Mode Collapse in LLM Generation via Geometric Regulation %A Xin Du %A Kumiko Tanaka-Ishii %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-du26m %I PMLR %P 26696--26714 %U https://proceedings.mlr.press/v306/du26m.html %V 306 %X Mode collapse is a persistent challenge in generative modeling and manifests in autoregressive text generation as behaviors ranging from explicit looping to gradual loss of diversity and premature trajectory convergence. We take a dynamical-systems view and reinterpret mode collapse as reduced state-space accessibility caused by geometric collapse: during generation, the model’s internal trajectory becomes confined to a low-dimensional region of its representation space. This implies mode collapse is not purely a token-level phenomenon and cannot be reliably mitigated by symbolic constraints or probability-only decoding heuristics. Guided by this perspective, we propose Reinforced Mode Regulation (RMR), a lightweight, online state-space intervention that regulates dominant self-reinforcing directions in the Transformer value cache (implemented as low-rank damping). Across multiple large language models, RMR substantially reduces mode collapse and enables stable, high-quality generation at extremely low entropy rates (down to 0.8 nats/step), whereas standard decoding typically collapses near 2.0 nats/step.
APA
Du, X. & Tanaka-Ishii, K.. (2026). Escaping Mode Collapse in LLM Generation via Geometric Regulation. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:26696-26714 Available from https://proceedings.mlr.press/v306/du26m.html.

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