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This paper proposes a lightweight evolutionary heuristic scheduler to optimize denoising trajectories in diffusion large language models, addressing failure modes like EOS Overflow and Proximal Bias, and outperforming baselines on reasoning and planning benchmarks.
This paper proposes a hybrid nested search framework that decouples structural sketching (by an LLM) from numeric parameter optimization (by traditional solvers like CMA-ES) in LLM-driven evolutionary optimization, and validates it across meta-optimization, code-based policies, and Bayesian inference tasks.
This paper proposes Hyper-ES, a subspace-based evolution strategy framework for LLM reasoning that obtains descent directions via lightweight gradient-based fine-tuning and then uses CMA-ES to merge layer-wise DARE-TIES coefficients, consistently outperforming GRPO-LoRA while requiring fewer gradient updates.
TinyRouter is a tiny 10K-parameter LLM router that learns to route each question to the best specialist model from a pool of open-source LLMs, using evolutionary training. It achieves performance matching or exceeding individual models on MMLU and math benchmarks.