Benchmark recommendations.

PiperOrigin-RevId: 607599420

README.md

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+## Benchmarks
+If you feel lost in the crowd of available optimizers for deep learning, there
+exist some extensive benchmarks:
+
+[Benchmarking Neural Network Training Algorithms, Dahl G. et al, 2023](https://arxiv.org/pdf/2306.07179),
+
+[Descending through a Crowded Valley — Benchmarking Deep Learning Optimizers, Schmidt R. et al, 2021](https://proceedings.mlr.press/v139/schmidt21a).
+
+If you are interested in developing your own benchmark for some tasks,
+consider the following framework
+
+[Benchopt: Reproducible, efficient and collaborative optimization benchmarks, Moreau T. et al, 2022](https://arxiv.org/abs/2206.13424).
+
+Finally, if you are searching for some recommendations on tuning optimizers,
+consider taking a look at
+
+[Deep Learning Tuning Playbook, Godbole V. et al, 2023](https://github.com/google-research/tuning_playbook).
+
+
 ## Citing Optax
 
 This repository is part of the DeepMind JAX Ecosystem, to cite Optax