arXiv AI
DART: Distributional Adversarial Recurrent Training for Algorithm Learning
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arXiv AI
Oct 2
Not Too Hard, Not Too Easy: Learning from Intermediate States for LLM Structured Reasoning
arXiv:2609.33149v2 Announce Type: replace Abstract: A common principle of effective learning is to practice material that is neither already mastered nor too difficult to permit progress. We ask how...
arXiv AI
Jul 7
CAP-CoT: Cycle Adversarial Prompt for Improving Chain of Thoughts in LLM Reasoning
arXiv:2604. 23270v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting has emerged as a simple and effective way to elicit step-by-step solutions from large language models (LLMs).
arXiv AI
Jul 1
From Search to Synthesis: Training LLMs as Zero-Shot Workflow Generators
arXiv:2606. 30704v1 Announce Type: cross Abstract: Large language models (LLMs) excel across a wide range of tasks, yet their instance-specific solutions often lack the structural consistency needed for reliable deployment.
arXiv AI
Jul 2
Right in the Right Way: LM Training with Verifiable Rewards and Human Demonstrations
arXiv:2607. 01181v1 Announce Type: cross Abstract: RL with verifiable rewards (RLVR) has emerged as a powerful paradigm for training LMs on tasks with well-defined success metrics, such as code generation and mathematical reasoning.
Hugging Face Trending Papers
Jun 17
Learning from Your Own Mistakes: Constructing Learnable Micro-Reflective Trajectories for Self-Distillation
Self-distillation improves reasoning in large language models by using the model's own rollouts as training signal, typically through implicit logit-level alignment that minimizes KL divergence toward a privileged target distribution. However, because this supervision is generated via uncontrolled sampling, it provides no diagnostic insight into the model's specific errors or corrective guidance for its individual failure patterns.
arXiv Computation and Language
Oct 27
Representation-Aligned Auxiliary Supervision for Language Model Adaptation
arXiv:2610.04098v2 Announce Type: replace Abstract: Language models exhibit strong reasoning capabilities, yet adapting them to structured domains remains challenging and can yield inconsistent outco...