METTA: Memory-Enhanced Test-Time Adaptation for Meta-Reinforcement Learning proposes a new approach that splits fast adaptation into two temporal scales: a causal neural memory for fine-grained within-episode adaptation with linear complexity, and compact episode-level summaries for long-range cross-episode inference. This design reduces the quadratic memory and computation costs of transformer-based agents while retaining expressive capacity for test-time training. Experiments on unseen Meta-World and PointMaze tasks show METTA achieves over 50% success on Meta-World and outperforms transformer baselines on long-context sparse-reward environments.
arXiv:2604. 00830v3 Announce Type: replace-cross Abstract: Test-Time Learning (TTL) enables language agents to iteratively refine their performance through repeated interactions with the environment at inference time.
arXiv:2607. 18830v1 Announce Type: cross Abstract: Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning global policy parameters that can be rapidly adapted to new tasks.
arXiv:2610.29671v1 Announce Type: new Abstract: Long-term agent memory is shifting from predefined workflows toward learnable policies for memory construction and retrieval. Existing learning-based m...
The paper introduces the AGI Maze Prediction Datasets and Benchmark, a lightweight, procedurally generated grid‑world testbed for evaluating predictive models, particularly Transformers, on tasks such as per‑step transition prediction, fixed‑horizon state prediction, and sequential textual‑observation prediction. It compares byte‑level Transformer baselines with two memory‑augmented architectures, showing that a pseudo‑video spatial‑memory Transformer achieves perfect validation accuracy on selected tasks and improves sequential text‑trace prediction, while a generic auxiliary latent‑memory Transformer does not consistently help. The study highlights that structured, task‑aligned working memory can be more effective than merely increasing latent capacity, and positions the benchmark as a compact setting for testing architectures that couple textual interfaces to learned structured state.
arXiv:2601. 19810v2 Announce Type: replace-cross Abstract: Unsupervised pre-training can equip reinforcement learning agents with prior knowledge and accelerate learning in downstream tasks.
The paper introduces TIDER, a time‑aware retrieval framework that uses a reinforcement‑learning trained temporal interval locator to identify relevant time windows for evidence retrieval in long‑horizon agent memory. By combining temporally grounded candidates with semantic retrieval and reranking them by proximity to the inferred windows, TIDER mitigates the displacement of target memories caused by temporally mismatched yet semantically similar entries. The lightweight 1.7B locator achieves state‑of‑the‑art accuracy on LifeBench and LoCoMo benchmarks, demonstrating the importance of temporal cues for effective evidence retrieval.
The paper introduces the AGI Maze Prediction Datasets and Benchmark, a lightweight testbed for evaluating how Transformers and other models learn world dynamics. The benchmark, built from procedurally generated grid worlds, includes per‑step transition prediction, fixed‑horizon state prediction, and sequential textual‑observation prediction, with source‑maze‑disjoint training and validation splits to test transferable action‑conditioned dynamics. Experiments show that a pseudo‑video spatial‑memory Transformer, which initializes and updates a two‑dimensional latent workspace from the input map and action history, achieves perfect validation accuracy on selected tasks and improves sequential text‑trace prediction, outperforming byte‑level and unstructured‑memory baselines and suggesting that structured, task‑aligned working memory is more effective than additional latent capacity alone.
arXiv:2507. 05257v4 Announce Type: replace-cross Abstract: Recent benchmarks for Large Language Model (LLM) agents primarily focus on evaluating reasoning, planning, and execution capabilities, while another critical component-memory, encompassing how agents memorize, update, and retrieve long-term information-is under-evaluated due to the lack of benchmarks.
All-in-One Memory (AiO-Mem) is a reinforcement learning framework that unifies memory retrieval, writing, and stopping decisions into a single reward space. It compiles diverse histories into traceable multi‑view events, uses a unified policy actor to generate interaction trajectories, and employs a trajectory‑conditioned process reward model trained with supervised fine‑tuning and DPO. Experiments on long‑context memory benchmarks show AiO‑Mem consistently improves answer accuracy compared to static memory systems and other RL‑trained agents.
arXiv:2607. 15880v1 Announce Type: cross Abstract: Imitation Learning aims to learn skills from extensive observations and demonstrations for robots, so it suffers from data scarcity and environment generalization.
arXiv:2509.23209v2 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) has emerged as an effective paradigm for test time adaptation to unseen tasks without parameter updates. H...
arXiv:2606. 05684v1 Announce Type: new Abstract: A central challenge for language agents is utilizing past experience to adapt to dynamic test-time conditions.