Filtering Learning Histories Enhances In-Context Reinforcement Learning

• URL: http://arxiv.org/abs/2505.15143v1
• Date: Wed, 21 May 2025 06:00:41 GMT
• Title: Filtering Learning Histories Enhances In-Context Reinforcement Learning
• Authors: Weiqin Chen, Xinjie Zhang, Dharmashankar Subramanian, Santiago Paternain,
• Abstract summary: Transformer models (TMs) have exhibited remarkable in-context reinforcement learning capabilities.<n>We propose a simple yet effective approach, learning history filtering (LHF) to enhance ICRL.<n>LHF is the first approach to avoid source suboptimality by dataset preprocessing.
• Score: 12.697029805927398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer models (TMs) have exhibited remarkable in-context reinforcement learning (ICRL) capabilities, allowing them to generalize to and improve in previously unseen environments without re-training or fine-tuning. This is typically accomplished by imitating the complete learning histories of a source RL algorithm over a substantial amount of pretraining environments, which, however, may transfer suboptimal behaviors inherited from the source algorithm/dataset. Therefore, in this work, we address the issue of inheriting suboptimality from the perspective of dataset preprocessing. Motivated by the success of the weighted empirical risk minimization, we propose a simple yet effective approach, learning history filtering (LHF), to enhance ICRL by reweighting and filtering the learning histories based on their improvement and stability characteristics. To the best of our knowledge, LHF is the first approach to avoid source suboptimality by dataset preprocessing, and can be combined with the current state-of-the-art (SOTA) ICRL algorithms. We substantiate the effectiveness of LHF through a series of experiments conducted on the well-known ICRL benchmarks, encompassing both discrete environments and continuous robotic manipulation tasks, with three SOTA ICRL algorithms (AD, DPT, DICP) as the backbones. LHF exhibits robust performance across a variety of suboptimal scenarios, as well as under varying hyperparameters and sampling strategies. Notably, the superior performance of LHF becomes more pronounced in the presence of noisy data, indicating the significance of filtering learning histories.

Related papers

• KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG [63.82127103851471]
  Retrieval-Augmented Generation (RAG) enables large language models to access broader knowledge sources.<n>We demonstrate that enhancing generative models' capacity to process noisy content is equally critical for robust performance.<n>We present KARE-RAG, which improves knowledge utilization through three key innovations.
  arXiv  Detail & Related papers  (2025-06-03T06:31:17Z)
• Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining [55.262510814326035]
  Existing reweighting strategies primarily focus on group-level data importance.<n>We introduce novel algorithms for dynamic, instance-level data reweighting.<n>Our framework allows us to devise reweighting strategies deprioritizing redundant or uninformative data.
  arXiv  Detail & Related papers  (2025-02-10T17:57:15Z)
• Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning [62.984693936073974]
  Value-based reinforcement learning can learn effective policies for a wide range of multi-turn problems.<n>Current value-based RL methods have proven particularly challenging to scale to the setting of large language models.<n>We propose a novel offline RL algorithm that addresses these drawbacks, casting Q-learning as a modified supervised fine-tuning problem.
  arXiv  Detail & Related papers  (2024-11-07T21:36:52Z)
• Dynamic Learning Rate for Deep Reinforcement Learning: A Bandit Approach [0.9549646359252346]
  In deep Reinforcement Learning (RL) models trained using gradient-based techniques, the choice of gradient and its learning rate are crucial to achieving good performance.<n>We propose dynamic Learning Rate for deep Reinforcement Learning (LRRL), a meta-learning approach that selects the learning rate based on the agent's performance during training.
  arXiv  Detail & Related papers  (2024-10-16T14:15:28Z)
• A Distribution-Aware Flow-Matching for Generating Unstructured Data for Few-Shot Reinforcement Learning [1.0709300917082865]
  We introduce a distribution-aware flow matching approach to generate synthetic unstructured data for few-shot reinforcement learning.<n>Our approach addresses key challenges in traditional model-based RL, such as overfitting and data correlation.<n>Results demonstrate that our method achieves stable convergence in terms of maximum Q-value while enhancing frame rates by 30% in the initial timestamps.
  arXiv  Detail & Related papers  (2024-09-21T15:50:59Z)
• CDSA: Conservative Denoising Score-based Algorithm for Offline Reinforcement Learning [25.071018803326254]
  Distribution shift is a major obstacle in offline reinforcement learning. Previous conservative offline RL algorithms struggle to generalize to unseen actions. We propose to use the gradient fields of the dataset density generated from a pre-trained offline RL algorithm to adjust the original actions.
  arXiv  Detail & Related papers  (2024-06-11T17:59:29Z)
• How Can LLM Guide RL? A Value-Based Approach [68.55316627400683]
  Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. Recent developments in large language models (LLMs) have showcased impressive capabilities in language understanding and generation, yet they fall short in exploration and self-improvement capabilities. We develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning.
  arXiv  Detail & Related papers  (2024-02-25T20:07:13Z)
• Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization [165.98557106089777]
  A key challenge is to enhance the capabilities of large language models (LLMs) amid a looming shortage of high-quality training data. Our study starts from an empirical strategy for the light continual training of LLMs using their original pre-training data sets. We then formalize this strategy into a principled framework of Instance-Reweighted Distributionally Robust Optimization.
  arXiv  Detail & Related papers  (2024-02-22T04:10:57Z)
• Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Environmental Systems [15.40286222692196]
  TAM-RL is a novel framework for few-shot learning in heterogeneous systems. We evaluate TAM-RL on two real-world environmental datasets.
  arXiv  Detail & Related papers  (2023-10-07T07:55:22Z)
• Provable Reward-Agnostic Preference-Based Reinforcement Learning [61.39541986848391]
  Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories. We propose a theoretical reward-agnostic PbRL framework where exploratory trajectories that enable accurate learning of hidden reward functions are acquired.
  arXiv  Detail & Related papers  (2023-05-29T15:00:09Z)
• Critic Regularized Regression [70.8487887738354]
  We propose a novel offline RL algorithm to learn policies from data using a form of critic-regularized regression (CRR) We find that CRR performs surprisingly well and scales to tasks with high-dimensional state and action spaces.
  arXiv  Detail & Related papers  (2020-06-26T17:50:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.