Curriculum Learning
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Related Articles from SNS
Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning
arXiv:2603.13761v2 Announce Type: replace Abstract: Curriculum learning--ordering training examples in a sequence to aid machine learning--takes inspiration from human learning, but has not gained widespread acceptance. Static strategies for scoring item difficulty rely on indirect proxy scores of varying quality and produce curricula that are not specific to the learner at hand. Dynamic approaches base difficulty estimates on gradient information, requiring considerable extra computation...
CLPO: Curriculum Learning meets Policy Optimization for LLM Reasoning
arXiv:2509.25004v2 Announce Type: replace Abstract: Online reinforcement learning with verifiable rewards (RLVR) has become an effective paradigm for improving the reasoning abilities of large language models, but most methods still optimize reasoning trajectories over the static problem set, wasting rollout budget on solved or overly difficult problems. We propose \textbf{CLPO (Curriculum Learning meets Policy Optimization)}, a self-evolving curriculum framework that uses on-policy rollout...
Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation
Announce Type: new Abstract: Telehealth systems have become increasingly important for delivering accessible and timely medical information. Existing large language models often struggle to provide consistent and contextually appropriate medical responses across varying levels of case severity. This limitation highlights the need for models that can effectively adapt to the progressive complexity in medical queries.
Self-Paced Curriculum Reinforcement Learning for Autonomous Superbike Racing in Simulation
Announce Type: new Abstract: Autonomous Racing has seen remarkable progress through deep Reinforcement Learning (RL), primarily for four-wheeled vehicles. However, motorbikes introduce substantially greater complexity due to the need to manage balance and lean angle, in addition to more reactive steering and throttle control, and a smaller weight. In this work, we present a framework for training an autonomous agent to race a superbike in VRider SBK, a physics-accurate Unity-based motorbike...
Curriculum-Adapted Robust Reinforcement Learning for UAV Deconfliction in Adversarial Environments
Announce Type: replace Abstract: Autonomous unmanned aerial vehicles (UAVs) increasingly rely on reinforcement learning (RL) for navigation. However, global navigation satellite system (GNSS) spoofing attacks can induce out-of-distribution observation shifts that corrupt value estimation and degrade mission performance. Existing robust RL approaches typically improve resilience against specific attack models but often fail to generalize to attacks not encountered during training.
SpectralTrain: A Universal Framework for Hyperspectral Image Classification
arXiv:2511.16084v3 Announce Type: replace Abstract: Hyperspectral image (HSI) classification typically involves large-scale data and computationally intensive training, which limits the practical deployment of deep learning models in real-world remote sensing tasks. This study introduces SpectralTrain, a universal, architecture-agnostic training framework that enhances learning efficiency by integrating curriculum learning (CL) with principal component analysis (PCA)-based spectral...
CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation
arXiv:2601.15408v2 Announce Type: replace Abstract: Medical vision-language models can automate the generation of radiology reports but struggle with accurate visual grounding and factual consistency. Existing models often misalign textual findings with visual evidence, leading to unreliable or weakly grounded predictions. We present CURE, an error-aware curriculum learning framework that improves grounding and report quality without any additional data.
Discovering autonomous quantum error correction via deep reinforcement learning
Announce Type: replace-cross Abstract: Quantum error correction is essential for fault-tolerant quantum computing. However, standard methods relying on active measurements may introduce additional errors. Autonomous quantum error correction (AQEC) circumvents this by utilizing engineered dissipation and drives in bosonic systems, but identifying practical encoding remains challenging due to stringent Knill-Laflamme conditions.
S2M-Trek: From Single to Multi-Sphere Transport via Per-Frame Deep Sets on a Wheel-Legged Robot
new Abstract: We study the problem of scaling dynamic loco-manipulation from a single free-rolling sphere to multiple spheres transported simultaneously on the back of a wheel-legged quadruped, without fences, grippers, or mechanical stops. Multiple identical free-rolling spheres form an unordered set with no persistent identity: their ordering may change independently at each history frame, creating a \emph{per-frame permutation symmetry} that standard history-concatenation set encoders do...
Another country to ban mobile phones in schools as reading levels fall
Another country to ban mobile phones in schools as reading levels fall Since 2023, Sweden’s centre-right coalition government has pursued a policy prioritising more reading time and less screen time - Bookmark Sweden, a nation long championed as a leader in adopting digital technology, is set to ban mobile phones in schools starting from the next academic year as part of a broad, international reversal on the use of screens in classrooms. Since 2023, the Scandinavian country’s centre-right...