Sample Efficiency: Accelerating AI Learning Across Robotics, LLMs, and Tabular Data
Latest 17 papers on sample efficiency: Jun. 13, 2026
The quest for greater sample efficiency is a persistent and crucial challenge across the AI/ML landscape. As models grow larger and tasks more complex, the cost in terms of data, compute, and human effort skyrockets. This makes breakthroughs in how quickly models can learn from limited examples incredibly valuable. Recent research highlights a fascinating trend: by cleverly integrating domain knowledge, architectural innovations, and refined training strategies, we can dramatically reduce the data hunger of our AI systems. Let’s dive into some of the latest advancements that are pushing the boundaries of sample-efficient learning.
The Big Idea(s) & Core Innovations
The central theme across these papers is intelligent learning – moving beyond brute-force data consumption to more strategic approaches. A key insight emerging from multiple works is the power of model-assisted or knowledge-guided learning. For instance, in robotics, Tufts University’s Codrin Crismariu and Ryan K. Cosner introduce MARCH: Model-Assisted Reinforcement Learning for the Perceptive Control of Humanoids over Sparse Footholds. They demonstrate that combining model-based reference trajectories with Control Lyapunov Function (CLF)-inspired rewards significantly boosts RL training sample efficiency for humanoid locomotion, reducing required episodes by half compared to purely model-free methods. This highlights that providing even simplified models of the world can offer invaluable guidance.
Similarly, in safe reinforcement learning for robotics, the work from Delft University of Technology and Southeast University, COP-Q: Safety-First Reinforcement Learning for Robot Control via Cholesky-Ordered Projection, shows that incorporating inter-objective covariance between reward and safety objectives (via Cholesky factorization) can adaptively reduce excessive conservatism on rewards while preserving safety. This leads to improved sample efficiency without compromising critical safety guarantees, especially when objectives are negatively correlated.
Another significant avenue for efficiency comes from structured reasoning and targeted optimization. JiaxuAN Chen and colleagues from Jinling Institute of Technology and China Agricultural University, in Structure from Reasoning, Numbers from Search: On-Premise Open LLMs as Structural Priors for Coupled MIMO Controller Tuning, show that on-premise LLMs can act as powerful structural priors for tuning complex industrial controllers. They found that LLMs excel not as numerical optimizers, but as reasoners identifying the correct “basin of attraction” in non-convex landscapes, dramatically improving reliability and reducing closed-loop evaluations up to 6x compared to global optimizers for complex plants. This division of labor – LLM for structure, optimizer for numbers – is a compelling hybrid approach.
For Large Language Models (LLMs) themselves, the focus is on smart data utilization and curriculum learning. The paper sGPO: Trading Inference FLOPs for Training Efficiency in RLVR by Shivchander Sudalairaj et al. from Red Hat and IBM, introduces sorted Group Policy Optimization (sGPO). It uses a single, cheap offline profiling pass to estimate query difficulty, which then intelligently filters data, allocates adaptive group sizes, and orders an easy-to-hard curriculum. This results in a remarkable 2.5-3.1x reduction in total training compute for reasoning tasks without performance loss. This echoes the sentiment from Yiming Zong and colleagues from Hong Kong University of Science and Technology in Cross-Epoch Adaptive Rollout Optimization for RL Post-Training, where their CERO framework adaptively allocates rollouts to prompts based on their informativeness (using Beta posterior expected Bernoulli variance), consistently outperforming fixed allocation and achieving substantial gains in math reasoning benchmarks.
Sample efficiency for LLMs also extends to practical applications like cybersecurity. Bernhard Kneip et al. in Sample-Efficient LLM-Based Detection of Malicious Web Server Logs with Forensically Explainable Reasoning introduce CEF-Log, a context-enhanced few-shot chain-of-thought prompting strategy. By teaching LLMs how to analyze logs through a five-step reasoning template, they achieve an F1-score of 0.99 with only 4 examples, a 10x improvement in sample efficiency over standard few-shot methods, while providing crucial forensic explanations.
Beyond LLMs and robotics, advancements are also seen in fundamental neural network architectures and multi-task learning. Ziyuan Li et al. from University of Applied Sciences Koblenz introduce Modeling Nonlinear Feature Interactions with Product-Unit Residual Networks. Their PURe networks, combining multiplicative product units with residual connections, explicitly model nonlinear feature interactions in tabular data, showing enhanced sample efficiency in low-data regimes (up to 29% error reduction) and improved interpretability. In wireless networks, Fatih Temiz et al. from the University of Ottawa demonstrate Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers. Their PromptDT framework leverages task-specific trajectory prompts to achieve up to 49% QoE improvement in multi-cell selection, generalizing across diverse network configurations without retraining, effectively replacing multiple specialized agents with a single unified model.
Under the Hood: Models, Datasets, & Benchmarks
These innovations are often built upon or validated by robust datasets and models:
- PC-Gym Process Control Simulators and Quadruple-Tank Process/3×3 Coupled Plant Benchmarks: Used for evaluating LLM-guided MIMO controller tuning (Structure from Reasoning…). Code available at https://github.com/cheer932041235/llm-onprem-control.
- Unitree G1 Humanoid Robot: Hardware demonstration for model-assisted locomotion over sparse footholds (MARCH). Video available at tinyurl.com/stepping-stones-corl26.
- DAPO-Math-17k and SciKnowEval Datasets: Crucial for evaluating LLM reasoning and compute efficiency in RLVR. Models like Qwen2.5-Math-1.5B/7B and Qwen3-4B-Instruct-2507 are heavily utilized (sGPO, CERO).
- CSIC 2010 and ForenWebLog Datasets: Benchmarks for malicious web server log detection using LLMs (Sample-Efficient LLM-Based Detection…).
- Brax and Safety-Gymnasium: Differentiable physics engines and unified benchmarks for robot locomotion and safe navigation in safe RL (COP-Q). Code available at https://github.com/RomainLITUD/COPQ.
- CT-RATE Dataset and PaliGemma 2: Used in CheXanatomy: Anatomy-Aware Vision-Language Modeling for Chest Radiographs from Stanford University to synthesize chest radiographs for anatomical supervision, enabling VLMs to generate segmentation masks via next-token prediction. Code available at https://github.com/sergiosgatidis/CheXanatomy and https://github.com/sergiosgatidis/CheXsynth.
- AlfWorld, WebShop, ScienceWorld Benchmarks: Used in Self-evolving LLM agents with in-distribution Optimization by Yudi Zhang et al. to train self-evolving LLM agents on long-horizon interactive tasks, achieving 50x better sample efficiency than baselines by learning process rewards from in-distribution critic and GAE.
- FinStressTS: A novel parametric synthetic benchmark from the National University of Singapore for time-series forecasting in finance, providing 30 diagnostic environments to evaluate model vulnerabilities to canonical financial mechanisms like volatility clustering and heavy tails. Code available at https://github.com/jiazeee/FinStressTS.
Impact & The Road Ahead
The implications of these advancements are far-reaching. Increased sample efficiency directly translates to faster development cycles, reduced computational costs, and the ability to tackle problems in data-scarce domains or where real-world interactions are expensive or risky (e.g., robotics, industrial control, and medicine). The hybrid approaches, combining symbolic reasoning with numerical optimization, or model-based planning with model-free learning, are particularly exciting as they leverage the strengths of different AI paradigms.
Looking forward, we can anticipate more sophisticated integration of domain knowledge and architectural inductive biases into our learning systems. The development of robust, generalizable learning agents that can adapt quickly to new tasks with minimal examples will be key to unlocking AI’s full potential in diverse real-world applications. From safer, more agile robots to more resilient and efficient LLMs, the path to truly intelligent AI is paved with sample efficiency. The journey continues with immense potential to democratize access to advanced AI capabilities and accelerate scientific discovery.
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