Author

Kajetan Schweighofer

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#artificial intelligence Preprint May 2026

Efficient Pre-Training of LLMs through Truncated SVD Representations

The TSVD (Truncated Singular Value Decomposition) framework which efficiently maintains orthonormality through QR decomposition and caching is presented, and a spectral energy heuristic is introduced to select the rank of the resulting low-rank weight matrices.

Kaivan Kamali, Kajetan Schweighofer, Hormoz Shahrzad et al. · 0 citations

Preprint Aug 2026

Beyond the Best Guess: Improving LLM Solution Coverage with Evolution Strategies

Evolution Strategies (ES), a population-based, gradient-free post-training method that optimizes directly in weight space through random perturbations, achieves consistently higher pass@k than RL and produces a broader output distribution with greater solution coverage.

Conor F. Hayes, Elliot Meyerson, Kajetan Schweighofer et al. · 2 citations