# Policy Gradients for Optimal Parallel Tempering MCMC

arXiv: https://arxiv.org/abs/2409.01574 · HTML: https://mlsanity.com/paper/2409.01574/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 1 of 1)

- [Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms](https://mlsanity.com/paper/1405.4758/) (2014)

## Built on by (most cited first, 1 of 1)

- [Refined Gradient-Based Temperature Optimization for the Replica-Exchange Monte-Carlo Method](https://mlsanity.com/paper/2601.13542/) (2026)

## Abstract (arXiv)

Parallel tempering is a meta-algorithm for Markov Chain Monte Carlo that uses multiple chains to sample from tempered versions of the target distribution, enhancing mixing in multi-modal distributions that are challenging for traditional methods. The effectiveness of parallel tempering is heavily influenced by the selection of chain temperatures. Here, we present an adaptive temperature selection algorithm that dynamically adjusts temperatures during sampling using a policy gradient approach. Experiments demonstrate that our method can achieve lower integrated autocorrelation times compared to traditional geometrically spaced temperatures and uniform acceptance rate schemes on benchmark distributions.