curriculum-learning

This paper proposes predicting task difficulty for LLM agents without running expensive rollouts, studying the problem across 17 agentic benchmarks and showing that token-level entropy is a useful predictive signal.

This paper introduces MSRT, a framework with a resource-aware Mixture of Speech Encoders (MoSE) to overcome the curse of multilinguality in many-to-many speech-to-text translation. The 4B-parameter model achieves state-of-the-art results across 45 languages, particularly improving low-resource speech translation with only 10 hours of paired data per language.

This arXiv paper introduces CVPO, a reinforcement learning method for LLMs that adapts value-variance for advantage estimation and uses dynamic curriculum learning to match question difficulty, achieving better reasoning performance than VAPO on math tasks.

This paper proposes DASH-OPD, a discrepancy-aware switching method for on-policy distillation in multi-turn LLM agent training, which adaptively toggles between teacher and student executors based on drift and recovery evidence to improve efficiency and performance.

CURV proposes a curriculum learning framework that enhances chart question answering by developing intrinsic visual grounded reasoning in multimodal LLMs, achieving significant improvements on real-world benchmarks.

This paper introduces KGPS, a Kalman-guided prompt selection method for adaptive RL finetuning of LLMs, which models prompt difficulty as a dynamic state to improve accuracy and rollout efficiency.

DHRCL proposes a reinforcement learning framework with dense hierarchical rewards and curriculum learning for training code LLMs, using syntax validation, execution success, unit-test pass rate, and AST structural similarity in a three-stage curriculum.

SCOUT introduces per-context reset curricula for sparse-reward reinforcement learning, adapting assistance removal to each context's learning progress, outperforming global pacing methods in navigation and manipulation tasks.

This paper proposes a fine-grained taxonomy for curriculum learning in NLP, separating difficulty evaluation from training scheduling to enable systematic analysis and comparison of CL strategies. It identifies an incomparability problem in prior work and provides a framework for designing and evaluating CL approaches.

The paper proposes A*-Inspired Batch Selection (A*-BS), a lightweight strategy that treats mini-batch scheduling as a heuristic search to improve CNN training efficiency. On MedMNIST tasks, a simple CNN with A*-BS outperforms deeper ResNet-18/50 baselines in accuracy and training speed.

This paper proposes a self-evolving in-context learning framework for direct pilot-to-beamformer design in multi-user MISO systems, integrating a Transformer backbone with a pilot encoder-decoder network and curriculum learning to handle multiple channel models without retraining.

EvoCUA-1.5 introduces an online reinforcement learning framework for multi-turn computer-use agents, achieving a 63.2% success rate on OSWorld-Verified and outperforming comparable open-weight models up to 35B parameters through step-level policy optimization and dynamic curriculum learning.

This paper introduces Progressive Code-Switching (PCS), a reinforcement learning approach with curriculum learning that gradually increases code-switching in LLMs to efficiently transfer multilingual reasoning capabilities.

This paper proposes a tone-conditioned curriculum learning framework for low-resource Bantu speech recognition, combining hybrid difficulty scoring, gated adapters, and staged curriculum training. Evaluations on six Southern Bantu languages show that W2V-BERT outperforms Whisper on Nguni languages while Whisper performs better on Sotho-Tswana languages.

SEAD introduces a competence-aware on-policy distillation method that uses entropy to guide supervision at token, temporal, and prompt levels, achieving a +4.8 average accuracy improvement on OLMo-3 across six math benchmarks.

This paper theoretically analyzes how curriculum learning, by decomposing complex problems into simpler sub-problems and composing solutions, can dramatically reduce the sample complexity of learning to simulate sequential computations (semiautomata) compared to direct methods, achieving subpolynomial supervision requirements in supervised fine-tuning and exponentially weaker coverage conditions in reinforcement learning with verifiable rewards.

GUICrafter introduces a weakly-supervised GUI agent that leverages massive unannotated screenshots and a two-stage curriculum learning framework to reduce reliance on expensive human annotations, achieving competitive performance with advanced systems like UI-TARS using only 0.1% of its data.

This paper proposes Transfer-Aware Curriculum (TAC), a bandit-style online curriculum for multi-domain RLVR that prioritizes domains whose updates benefit other domains using gradient-geometry alignment. TAC improves macro-averaged accuracy on Qwen3-1.7B and Llama3.2-3B over fixed and learnability-only curricula.

Meta's new paper 'Autodata' introduces an agentic data scientist that generates and meta-optimizes synthetic training data, significantly outperforming standard methods and enabling a small 4B model to beat a 397B baseline in legal tasks.

This paper introduces Lie-bracket prediction of transfer order for sequential learning, using commutators of gradient fields to determine pairwise order and scaling to many domains. Experiments show high accuracy in predicting optimal curriculum orders for fine-tuning and instruction tuning.