> ## Content Index
> Fetch the complete content index at: https://alpha1.ghost.io/llms.txt
> Use this file to discover other available public pages before exploring further.

# Papers That Quants Read (NeurIPS, ICML, ICLR)
- URL: https://alpha1.ghost.io/papers-that-quants-read-neurips-icml-iclr/
- Published: 2026-10-27T07:00:00.000Z
- Updated: 2026-10-29T21:46:22.000Z
- Author: alpha1

This is a curated collection of papers we have read (me and other quant peers), some recently and some a while back, that have shaped our thinking and sparked ideas in our own research. Every paper here was published at one of the top-tier machine learning conferences - NeurIPS, ICML, and ICLR. We think publishing at these venues is one of the most underrated moves a student or job candidate can make. To us, a paper at one of these conferences is a golden ticket and arguably the fastest way to get noticed by a quant firm. 

It's no coincidence that the biggest names in quantitative finance sponsor these events year after year, including Citadel, Citadel Securities, D. E. Shaw, Hudson River Trading, Jane Street, Jump Trading, Optiver, Susquehanna, G Research, IMC, XTX Markets, Point72, QRT, Two Sigma, and PDT Partners. When these firms show up, it's for a simple reason. They're looking for their next quant.

Enjoy reading them 

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### NeurIPS (Neural Information Processing Systems)

Ahn, K., Xu, B., Abreu, N., Fan, Y., Magakyan, G., Sharma, P., Zhan, Z. and Langford, J., 2025\. [Dion: Distributed orthonormalized updates](https://arxiv.org/abs/2504.05295?ref=alpha1.ghost.io). *arXiv preprint arXiv:2504.05295* [also here](https://neurips.cc/virtual/2025/loc/san-diego/128655?ref=alpha1.ghost.io)

Doumont, C., Picheny, V., Borovitskiy, V.S. and Moss, H., 2026\. [Omnipresent yet overlooked: Heat kernels in combinatorial Bayesian optimization.](https://arxiv.org/abs/2510.26633?ref=alpha1.ghost.io) *Advances in Neural Information Processing Systems*, *38*, pp.151952-151990.

Liu, S., Yuan, H., Hu, M., Li, Y., Chen, Y., Liu, S., Lu, Z. and Jia, J., 2024\. [Rl-gpt: Integrating reinforcement learning and code-as-policy.](https://arxiv.org/abs/2402.19299?ref=alpha1.ghost.io) *Advances in Neural Information Processing Systems*, *37*, pp.28430-28459.

Oh, G., Cho, W., Kim, S., Choi, S. and Yu, Y., 2026\. [Revisiting Residual Connections: Orthogonal Updates for Stable and Efficient Deep Networks.](https://arxiv.org/abs/2505.11881?ref=alpha1.ghost.io) *Advances in Neural Information Processing Systems*, *38*, pp.71725-71759.

Puri, I., Sudalairaj, S., Xu, G., Bhandwaldar, A., Xu, K. and Srivastava, A., 2026\. [Rollout roulette: A probabilistic inference approach to inference-time scaling of llms using particle-based monte carlo methods.](https://arxiv.org/abs/2502.01618?ref=alpha1.ghost.io) *Advances in Neural Information Processing Systems*, *38*, pp.156427-156454.

Wang, K., Javali, I., Bortkiewicz, M., Trzcinski, T. and Eysenbach, B., 2026\. [1000 layer networks for self-supervised rl: Scaling depth can enable new goal-reaching capabilities.](https://arxiv.org/abs/2503.14858?ref=alpha1.ghost.io) *Advances in Neural Information Processing Systems*, *38*, pp.157643-157670.

Zoltowski, D., Wu, S., Gonzalez, X., Kozachkov, L. and Linderman, S., 2026\. [Parallelizing mcmc across the sequence length.](https://arxiv.org/abs/2508.18413?ref=alpha1.ghost.io) *Advances in Neural Information Processing Systems*, *38*, pp.22242-22277.

### ICML (International Conference on Machine Learning)

Benyamine, A., Grand-Clément, J., Petrik, M., Jordan, M.I. and Durmus, A., 2026\. [Dynamic Programming for Epistemic Uncertainty in Markov Decision Processes.](https://arxiv.org/abs/2602.03381?ref=alpha1.ghost.io) *arXiv preprint arXiv:2602.03381*.

Cagnetta, F., Raventós, A., Ganguli, S. and Wyart, M., 2026\. [Deriving neural scaling laws from the statistics of natural language](https://arxiv.org/abs/2602.07488?ref=alpha1.ghost.io). *arXiv preprint arXiv:2602.07488*.

Chu, T., Zhai, Y., Yang, J., Tong, S., Xie, S., Schuurmans, D., Le, Q.V., Levine, S. and Ma, Y., 2025\. [Sft memorizes, rl generalizes: A comparative study of foundation model post-training.](https://arxiv.org/abs/2501.17161?ref=alpha1.ghost.io) *arXiv preprint arXiv:2501.17161*.

Do, G., Le, H. and Tran, T., 2026\. [Eigenvectors of Experts are Training-free Non-collapsing Routers](https://arxiv.org/abs/2605.30992?ref=alpha1.ghost.io). *arXiv preprint arXiv:2605.30992*.

Gu, J., Qiao, Z. and Luo, X. 2026\. [Detecting the semantic fixed point: a geometric framework for efficient inference](https://openreview.net/pdf?id=DACN5xM4h7&ref=alpha1.ghost.io), *Proceedings of the 43rd International Conference on Machine Learning*.

Oufattole, N., McDermott, M.B.A. and Stultz, C. 2026\. [MoRGen: Mixture-of-Resolutions Generative Forecasting for Irregularly Sampled Medical Time-Series Data](https://openreview.net/forum?id=Hvqkdu4Luv&ref=alpha1.ghost.io), in *Forty-third International Conference on Machine Learning*.

Turkoglu, M.O., Mühlematter, D.J., Becker, A., Schindler, K. and Aasen, H., 2026\. [Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles](https://arxiv.org/abs/2601.22068?ref=alpha1.ghost.io). *arXiv preprint arXiv:2601.22068*.

### ICLR (International Conference on Learning Representations)

Amsel, N., Persson, D., Musco, C. and Gower, R.M. (2026) ‘[The Polar Express: optimal matrix sign methods and their application to the Muon algorithm](https://openreview.net/forum?id=yRtgZ1K8hO&ref=alpha1.ghost.io)’, *The Fourteenth International Conference on Learning Representations*. 

Bergsträßer, P., Cotterell, R. and Lin, A.W. (2026) ‘[Transformers are inherently succinct’](https://openreview.net/forum?id=Yxz92UuPLQ&ref=alpha1.ghost.io), *The Fourteenth International Conference on Learning Representations*.

Danieli, F., Rodriguez, P., Sarabia, M., Suau, X. and Zappella, L. (2026) ‘[ParaRNN: unlocking parallel training of nonlinear RNNs for large language models](https://openreview.net/forum?id=mX8b64iUaa&ref=alpha1.ghost.io)’, *The Fourteenth International Conference on Learning Representations*. 

Dekoninck, J., Petrov, I., Minchev, K., Marinov, M., Drencheva, M., Konova, L., Shumanov, M., Tsvetkov, K., Drenchev, N., Todorov, L. and Nikolova, K., 2026, April. [The open proof corpus: A large-scale study of llm-generated mathematical proofs.](https://arxiv.org/abs/2506.21621?ref=alpha1.ghost.io) In *International Conference on Learning Representations* (Vol. 2026, pp. 22214-22244). 

Kim, K., Kotha, S., Liang, P. and Hashimoto, T., 2026, April. [Pre-training under infinite compute.](https://arxiv.org/abs/2509.14786?ref=alpha1.ghost.io) In *International Conference on Learning Representations* (Vol. 2026, pp. 74596-74636).

Laban, P., Hayashi, H., Zhou, Y. and Neville, J. (2026) ‘[LLMs Get Lost In Multi-Turn Conversation](https://openreview.net/forum?id=VKGTGGcwl6&ref=alpha1.ghost.io)’, in *The Fourteenth International Conference on Learning Representations*

Liang, K., Chen, L., Liu, B. and Liu, Q., 2026, April. [Cautious optimizers: Improving training with one line of code.](https://arxiv.org/abs/2411.16085?ref=alpha1.ghost.io) In *International Conference on Learning Representations* (Vol. 2026, pp. 106538-106563).