# RIFAR: Reliability and Forgetting-Aware Replay for Continual Robot Learning

arXiv: https://arxiv.org/abs/2610.03079 · HTML: https://mlsanity.com/paper/2610.03079/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 15 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Builds on (most cited first, 15 of 15)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning](https://mlsanity.com/paper/2601.16163/) (2026)
- [Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets](https://mlsanity.com/paper/2504.02792/) (2025)
- [CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion](https://mlsanity.com/paper/2601.09512/) (2026)
- [Simple Recipe Works: Vision-Language-Action Models are Natural Continual Learners with Reinforcement Learning](https://mlsanity.com/paper/2603.11653/) (2026)
- [CRL-VLA: Continual Vision-Language-Action Learning](https://mlsanity.com/paper/2602.03445/) (2026)
- [Can Vision-Language-Action Models Learn from Real-World Data Continually without Forgetting?](https://mlsanity.com/paper/2605.26820/) (2026)
- [World Action Models Enable Continual Imitation Learning with Recurrent Generative Replays](https://mlsanity.com/paper/2606.27374/) (2026)
- [Information-Theoretic Constraints for Continual Vision-Language-Action Alignment](https://mlsanity.com/paper/2603.13335/) (2026)
- [PHASER: Phase-Aware and Semantic Experience Replay for Vision-Language-Action Models](https://mlsanity.com/paper/2606.03598/) (2026)
- [LayerRoute: Action-Conditioned Mixture-of-Layers Routing for Vision-Language-Action Policies](https://mlsanity.com/paper/2609.06079/) (2026)

## Abstract (arXiv)

Genuine embodied agency requires robots to turn continuous real-world experience into lasting, transferable skills. This demands continual learning that integrates new capabilities without eroding prior knowledge as tasks and environments evolve. Experience replay mitigates forgetting, but storing complete demonstrations becomes costly as tasks accumulate. World-action models offer a generative alternative, reconstructing past experience through joint predictions of actions and future observations. However, visually coherent rollouts may contain actions that cannot realize the predicted transitions, while new-task adaptation can disrupt previously learned behavior. RIFAR therefore combines reliability screening with drift-aware replay selection. It reconstructs trajectories from compact demonstration prefixes and uses a frozen inverse-dynamics model to assess action-visual consistency. Training first combines current demonstrations with the highest-quality screened trajectories. RIFAR then compares action predictions before and after this adaptation on identical historical inputs, reselecting trajectories with larger normalized drift from the same screened pool for continued training. Across three LIBERO suites and real-world experiments, RIFAR surpasses the previous state of the art in WAM-based generative replay. On LIBERO-Goal, it achieves 90.97 AUC while retaining only 320 historical time steps per task, approximately 4.9% of the steps retained using 50-demonstration replay.