Sample Efficiency: Unlocking Faster, Smarter AI Across Diverse Domains

Latest 50 papers on sample efficiency: Oct. 12, 2025

In the fast-evolving landscape of AI and Machine Learning, sample efficiency stands as a critical frontier. It’s the ability of a model to learn effectively from fewer data samples, translating directly to reduced computational costs, faster iteration cycles, and enhanced applicability in data-scarce real-world scenarios. Recent breakthroughs, as evidenced by a wave of innovative research, are pushing the boundaries of what’s possible, from making large language models more robust to enabling robots to learn with minimal demonstrations. This post dives into these exciting advancements, highlighting the core ideas that are shaping the future of efficient AI.

The Big Ideas & Core Innovations

The quest for sample efficiency is driving ingenious solutions across various AI disciplines. A central theme emerging from these papers is the strategic infusion of prior knowledge and structured learning to guide agents and models more effectively.

For instance, in reinforcement learning (RL), several papers focus on enhancing how agents perceive and interact with their environments. Researchers from the University of California, Davis and Berkeley, in their paper “Gaze on the Prize: Shaping Visual Attention with Return-Guided Contrastive Learning”, introduce a learnable foveal attention mechanism. This system uses return-guided contrastive learning to direct an agent’s visual focus to task-relevant features, significantly boosting sample efficiency without altering the core RL algorithm. Similarly, “Oracle-Guided Masked Contrastive Reinforcement Learning for Visuomotor Policies” by University of Example and Institute of Advanced Robotics integrates human-like oracle feedback with masked contrastive learning, leading to more efficient visuomotor policy training and better generalization.

Beyond perception, intent and adaptability are key. From Princeton University and UC Berkeley, the paper “Intention-Conditioned Flow Occupancy Models” (InFOM) leverages intention-conditioned flow occupancy models, pre-training RL agents to capture long-term dependencies and user intentions. This results in more robust and sample-efficient learning. For real-world deployment, especially in critical areas like autonomous driving, human involvement proves invaluable. The framework presented by Tsinghua University, China in “From Learning to Mastery: Achieving Safe and Efficient Real-World Autonomous Driving with Human-In-The-Loop Reinforcement Learning” demonstrates how human-in-the-loop reinforcement learning (HILRL) bridges the simulation-to-reality gap, improving safety and training efficiency.

In the realm of Large Language Models (LLMs), the focus shifts to robust fine-tuning and exploration. OATML, University of Oxford and OXCAV, University of Oxford introduce CAPO in “Stabilizing Policy Gradients for Sample-Efficient Reinforcement Learning in LLM Reasoning”, a policy optimization framework that uses second-order geometry to stabilize RL training in LLMs, yielding up to 30x sample efficiency improvements. Further enhancing LLM performance, Nanjing University and Shanghai AI Laboratory propose DIVER in “Diversity-Incentivized Exploration for Versatile Reasoning”, a framework that incentivizes deep exploration via global sequence-level diversity, tackling sparse rewards in reasoning tasks.

The challenge of efficient fine-tuning for LLMs also sees new solutions. From The University of Texas at Austin and Vietnam National University, Ho Chi Minh City, “DoRAN: Stabilizing Weight-Decomposed Low-Rank Adaptation via Noise Injection and Auxiliary Networks” and “HoRA: Cross-Head Low-Rank Adaptation with Joint Hypernetworks” introduce novel Parameter-Efficient Fine-Tuning (PEFT) methods. DoRAN enhances stability and expressiveness through noise injection and hypernetworks, outperforming LoRA and DoRA. HoRA promotes cross-head information sharing in multi-head self-attention via joint hypernetworks, achieving polynomial rate sample efficiency improvements over LoRA. These developments significantly reduce computational overhead while maintaining high performance. Similarly, University of Georgia’s “Fine-tuning LLMs with variational Bayesian last layer for high-dimensional Bayesian optimization” introduces LoRA-VBLL, which combines LoRA with variational Bayesian last layers for scalable uncertainty estimation in high-dimensional Bayesian optimization.

Even in black-box optimization and control, innovative techniques are emerging. LERIA, Université d’Angers presents an “Black-Box Combinatorial Optimization with Order-Invariant Reinforcement Learning”, which improves sample efficiency by avoiding explicit variable dependency graphs. For dynamic systems, “Latent Mixture of Symmetries for Sample-Efficient Dynamic Learning” from Arizona State University introduces Latent MoS, a model that leverages symmetry to learn from sparse and low-resolution data, providing theoretical guarantees for symmetry preservation.

Under the Hood: Models, Datasets, & Benchmarks

These innovations are often powered by novel architectures, specially crafted datasets, and rigorous benchmarking. Here’s a look at some of the key resources driving this progress:

• Relational Transformer (RT): Introduced by Stanford University and SAP in “Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data”, RT tokenizes database cells with metadata and uses a novel relational attention mechanism for zero-shot learning. It utilizes RelBench datasets for pretraining and evaluation, with code available at GitHub repository.
• InFOM: From Princeton University and UC Berkeley, this framework for reinforcement learning is detailed in “Intention-Conditioned Flow Occupancy Models”, with code on GitHub.
• BFS-Prover: A scalable framework for automatic theorem proving using LLMs, discussed by ByteDance Seed and Stanford University in “BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving”. It’s evaluated on the MiniF2F benchmark.
• LEXPOL: Developed by University of Massachusetts Amherst for multi-task reinforcement learning, as described in “Multi-Task Reinforcement Learning with Language-Encoded Gated Policy Networks”. It’s benchmarked on MetaWorld tasks, with code at GitHub.
• DyMoDreamer: A model-based RL algorithm from Beijing Institute of Technology and Tsinghua University detailed in “DyMoDreamer: World Modeling with Dynamic Modulation”, achieving SOTA on Atari 100k, DeepMind Visual Control Suite, and Crafter benchmark. Code is available on GitHub.
• ZEROSHOTOPT: A pretrained model for efficient black-box optimization introduced by MIT and MIT-IBM Watson AI Lab in “ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization”. It uses a synthetic function generator based on Gaussian processes for pretraining, with code on GitHub.
• TimeRewarder: A reward learning method from Tsinghua University and University of Pennsylvania presented in “TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance”, demonstrated on robotic manipulation benchmarks.
• LPM: An intrinsic motivation method for noise-robust exploration from University of California, Merced in “Beyond Noisy-TVs: Noise-Robust Exploration Via Learning Progress Monitoring”, with code on GitHub.
• BRIDGE: An algorithm for fine-tuning behavioral cloning policies with preference-based RL, detailed by University of Zurich and ETH AI Center in “Fine-tuning Behavioral Cloning Policies with Preference-Based Reinforcement Learning”, with code at GitHub.
• WISDOM: A framework from Beijing Institute of Technology and Microsoft Research AI Frontiers for non-stationary RL, discussed in “Wavelet Predictive Representations for Non-Stationary Reinforcement Learning”, with code on GitHub.
• POLO: A novel RL framework for lead optimization from Northwestern University and University of Wisconsin–Madison in “POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimization” for molecular property enhancement.

Impact & The Road Ahead

The collective impact of this research is profound. These advancements are paving the way for AI systems that are not only more powerful but also more practical and sustainable. By drastically cutting down on data requirements, we can accelerate research, reduce the carbon footprint of AI training, and democratize access to sophisticated models for smaller organizations and less-resourced domains. Imagine medical AI trained on fewer patient records, or robots learning new tasks with just a handful of demonstrations.

The road ahead points towards more integrated frameworks where diverse techniques, such as contrastive learning, human-in-the-loop feedback, and information-theoretic principles, are synergistically combined to address complexity. The focus will likely remain on developing foundational models that generalize across tasks and domains with minimal fine-tuning, such as the Relational Transformer for zero-shot learning on relational data. Furthermore, understanding the scaling behaviors of LLMs under various constraints, as explored in “Scaling Behaviors of LLM Reinforcement Learning Post-Training” by a collaboration of University of Science and Technology of China and Shanghai AI Laboratory, will be crucial for efficient resource allocation and practical deployment.

These papers highlight a vibrant field, constantly innovating to make AI more accessible, efficient, and aligned with real-world needs. The future promises AI systems that learn with an unprecedented economy of data, pushing us closer to truly intelligent and adaptable machines.