Escaping Mode Collapse in LLM Generation via Geometric Regulation
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Computer Science > Computation and Language
Title:Escaping Mode Collapse in LLM Generation via Geometric Regulation
Abstract:Mode collapse is a persistent challenge in generative modeling and appears 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 solved 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 generation at extremely low entropy rates (down to 0.8 nats/step), whereas standard decoding typically collapses near 2.0 nats/step.
| Comments: | Accepted to ICML 2026 |
| Subjects: | Computation and Language (cs.CL); Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI); Chaotic Dynamics (nlin.CD) |
| Cite as: | arXiv:2605.00435 [cs.CL] |
| (or arXiv:2605.00435v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.00435
arXiv-issued DOI via DataCite
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Submission history
From: Xin Du PhD [view email][v1] Fri, 1 May 2026 06:12:05 UTC (5,335 KB)
[v2] Wed, 27 May 2026 13:56:08 UTC (10,637 KB)
[v3] Fri, 31 Jul 2026 05:11:04 UTC (5,338 KB)
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