Sample Efficiency: Unlocking Faster, Smarter AI Learning Across Robotics, LLMs, and More

Latest 19 papers on sample efficiency: Jan. 17, 2026

The quest for more intelligent and autonomous AI agents often runs into a fundamental roadblock: sample efficiency. Training cutting-edge models, whether in robotics, large language models (LLMs), or complex control systems, typically demands vast amounts of data and computational resources. This makes real-world deployment challenging and innovation slow. But what if our AI could learn more from less? Recent breakthroughs, synthesized from a diverse collection of research papers, are showing us exactly how, by refining everything from human feedback mechanisms to the very architecture of learning itself.

The Big Idea(s) & Core Innovations

At the heart of these advancements is a collective push to make AI learning more targeted, adaptive, and interpretable. A recurring theme is the move towards more intelligent data utilization and feedback mechanisms. For instance, researchers from Frontiers in Robotics and AI and the University of Montreal, in their paper “Adaptive Querying for Reward Learning from Human Feedback”, propose adaptive querying. This method significantly enhances reward learning by selecting informative and user-aligned human feedback, thereby accelerating the learning process for autonomous agents. This echoes the sentiment in “Learning Contextually-Adaptive Rewards via Calibrated Features” by Alexandra Forsey-Smerek, Julie Shah, and Andreea Bobu from the Massachusetts Institute of Technology. They highlight that by explicitly modeling how contextual factors influence feature saliency, their calibrated features framework learns from targeted human feedback with vastly improved sample efficiency, requiring 10x fewer queries for equivalent accuracy.

In the realm of reinforcement learning (RL) and long-horizon tasks, innovation centers on structured exploration and robust credit assignment. Yuanlin Duan et al. from Rutgers University introduce Cago in “Learning from Demonstrations via Capability-Aware Goal Sampling”. This adaptive curriculum-based imitation learning framework enables steady progress in complex, sparse-reward environments by dynamically aligning the agent’s goals with its evolving capabilities. Similarly, Celeste Veronese et al. from the University of Verona propose a neuro-symbolic approach in “Sample-Efficient Neurosymbolic Deep Reinforcement Learning”. By integrating symbolic knowledge as logical rules derived from simpler tasks, they guide DRL agents, drastically improving sample efficiency and generalization in sparse-reward settings. Further enhancing RL efficiency, M. Jalaeian-Farimani and O. S. Fard present “Enhanced-FQL(λ), an Efficient and Interpretable RL with novel Fuzzy Eligibility Traces and Segmented Experience Replay”, which uses fuzzy eligibility traces for more flexible credit assignment and segmented experience replay to focus on relevant experiences.

For LLMs, the challenge lies in long contexts and inference-time adaptation. Xue Gong et al. from Tencent introduce Segmental Advantage Estimation (SAE) in “Segmental Advantage Estimation: Enhancing PPO for Long-Context LLM Training”. SAE tackles bias in PPO for long-context LLMs by focusing advantage computation on semantically meaningful segment transitions, leading to more stable training and better performance. Intriguingly, Andrew J. Kiruluta from UC Berkeley School of Information offers a foundational shift in understanding in “Filtering Beats Fine Tuning: A Bayesian Kalman View of In Context Learning in LLMs”, proposing that in-context learning can be rigorously derived as Bayesian state estimation using Kalman filtering, providing stability guarantees and insights into uncertainty dynamics.

Robotics and complex systems benefit from sophisticated simulation, collaborative frameworks, and novel experience management. J. Hu et al. from the Robotics and Perception Group, University of Zurich, in “Learning Quadrotor Control From Visual Features Using Differentiable Simulation”, show how differentiable simulation and visual features can efficiently train quadrotor controllers. For multi-robot systems, a paper on “Low-Altitude Satellite-AAV Collaborative Joint Mobile Edge Computing and Data Collection via Diffusion-based Deep Reinforcement Learning” leverages diffusion-based deep RL to optimize resource allocation in dynamic, real-time edge computing. Roya Khalili Amirabadi et al. present SODACER in “Self-Organizing Dual-Buffer Adaptive Clustering Experience Replay (SODASER) for Safe Reinforcement Learning in Optimal Control”, a framework that improves memory efficiency and accelerates convergence for safe optimal control in nonlinear systems by dynamically pruning redundant experiences.

Under the Hood: Models, Datasets, & Benchmarks

These papers showcase the power of innovative frameworks and resource utilization:

• Cago (code): A curriculum-based imitation learning framework that adapts goal sampling based on agent capabilities, evaluated in sparse-reward environments.
• Differentiable Simulation: Utilized by J. Hu et al. (code) for training quadrotor controllers, demonstrating effectiveness with visual features and reducing reliance on traditional sensor data.
• JUDGEFLOW (code): An agentic workflow optimization pipeline from KAIST, Radical Numerics, and Omelet that employs reusable logic blocks and a Judge module for fine-grained error localization in LLM-based applications, evaluated on mathematical reasoning and code generation.
• SAE (Segmental Advantage Estimation): A PPO enhancement for long-context LLM training, filtering noisy value predictions by leveraging semantically coherent segments, showing improvements across various model sizes in mathematical problem-solving tasks.
• SODACER: A dual-buffer experience replay with self-organizing adaptive clustering and Control Barrier Functions (CBFs) for safe optimal control, validated on a real-world HPV transmission model.
• GTL-CIRL: A framework by Hadi Partovi Aria and Zhe Xu from Arizona State University that combines RL with Causal Graph Temporal Logic and Gaussian Process-driven Bayesian optimization, improving efficiency in gene and power network studies.
• QNTK-UCB: From National University of Singapore and University of Birmingham, this algorithm leverages quantum neural tangent kernels for superior sample efficiency and regret bounds in contextual bandit problems in low-data regimes.
• FlowRL: Proposed by Mohammad Pivezhandi and Abusayeed Saifullah from Wayne State University and University of Texas at Dallas, it uses continuous normalizing flows to generate synthetic semi-structured sensor data for few-shot RL tasks, demonstrating effectiveness in Dynamic Voltage and Frequency Scaling (DVFS) with higher frame rates.
• NMNC (code): Introduced by Byungwoo Kang et al. from Harvard Medical School, this Neural Manifold Noise Correlation approach improves credit assignment by leveraging the neural manifold, showing performance gains across various architectures and datasets like ImageNet.
• COVR: A collaborative optimization framework from Sun Yat-sen University and Peking University integrating VLMs and RL for visual-based control, featuring an Exploration-Driven Dynamic Filter (EDDF) and Return-Aware Adaptive Loss Weight (RALW) modules, validated on CARLA and DMControl.
• HiDVFS: A hierarchical multi-agent DVFS scheduler from Iowa State University and University of Texas at Dallas for OpenMP DAG workloads, evaluated on NVIDIA Jetson TX2 using BOTS benchmarks, achieving significant speedup and energy reduction.
• Revolve2 (code): A platform enabling research into sample inheritance in Bayesian optimization for evolutionary robotics, as explored by K. Ege de Bruin et al. from the University of Oslo.

Impact & The Road Ahead

The collective impact of this research is profound, promising AI systems that are not only more capable but also more efficient, adaptable, and interpretable. Faster learning from less data means quicker development cycles, reduced computational costs, and more practical deployment in resource-constrained environments like edge devices or real-world robotics. The innovations in human feedback and neuro-symbolic integration are leading to agents that learn from us more intelligently, making human-AI collaboration more intuitive and effective. For LLMs, advances in long-context training and a deeper theoretical understanding of in-context learning pave the way for more robust and reliable large models.

Looking ahead, the integration of these diverse approaches holds immense potential. We could see robots learning complex tasks with minimal human intervention, LLMs adapting to new information in real-time with guaranteed stability, and multi-agent systems coordinating optimally in dynamic environments. The open questions revolve around generalizing these specialized solutions to broader domains, further enhancing the synergy between different learning paradigms, and extending theoretical guarantees to real-world complexities. The path to truly intelligent, efficient, and ethical AI systems is being paved by these advancements in sample efficiency, and the future of AI learning looks brighter than ever.