# TractRLFusion: A GPT-Based Multi-Critic Policy Fusion Framework for Fiber Tractography

arXiv: https://arxiv.org/abs/2601.13897 · HTML: https://mlsanity.com/paper/2601.13897/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [TractSeg - Fast and accurate white matter tract segmentation](https://mlsanity.com/paper/1805.07103/) (2018)
- [DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography](https://mlsanity.com/paper/1812.05129/) (2018)
- [TractOracle: towards an anatomically-informed reward function for RL-based tractography](https://mlsanity.com/paper/2403.17845/) (2024)
- [What Matters in Reinforcement Learning for Tractography](https://mlsanity.com/paper/2305.09041/) (2023)
- [Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization](https://mlsanity.com/paper/2403.19462/) (2024)
- [Farthest Streamline Sampling for the Uniform Distribution of Forearm Muscle Fiber Tracts from Diffusion Tensor Imaging](https://mlsanity.com/paper/2306.13969/) (2023)
- [Tract-RLFormer: A Tract-Specific RL policy based Decoder-only Transformer Network](https://mlsanity.com/paper/2411.05757/) (2024)
- [Exploring the robustness of TractOracle methods in RL-based tractography](https://mlsanity.com/paper/2507.11486/) (2025)

## Abstract (arXiv)

Tractography plays a pivotal role in the non-invasive reconstruction of white matter fiber pathways, providing vital information on brain connectivity and supporting precise neurosurgical planning. Although traditional methods relied mainly on classical deterministic and probabilistic approaches, recent progress has benefited from supervised deep learning (DL) and deep reinforcement learning (DRL) to improve tract reconstruction. A persistent challenge in tractography is accurately reconstructing white matter tracts while minimizing spurious connections. To address this, we propose TractRLFusion, a novel GPT-based policy fusion framework that integrates multiple RL policies through a data-driven fusion strategy. Our method employs a two-stage training data selection process for effective policy fusion, followed by a multi-critic fine-tuning phase to enhance robustness and generalization. Experiments on HCP, ISMRM, and TractoInferno datasets demonstrate that TractRLFusion outperforms individual RL policies as well as state-of-the-art classical and DRL methods in accuracy and anatomical reliability.