A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Changle Qu, Sunhao Dai, Hengyi Cai, et al.
TurnSight is a turn-level hindsight self-distillation framework for tool-integrated reasoning that derives supervision from execution-conditioned hindsight and cross-horizon agreement to improve credit assignment.
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This paper proposes Chain-of-Thought (CoT) supervision to improve long-context language models by training them to reason over dispersed information before generating answers.
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This paper explores how large language models can self-improve through iterative fine-tuning on their own outputs, reducing the need for extensive human supervision.
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This paper introduces unsupervised process reward models that generate step-level supervision for LLM reasoning without human labels, improving performance on math reasoning tasks.
Tej Deep Pala, Panshul Sharma, Amir Zadeh, et al.
This paper introduces error-aware hierarchical supervision for process reward models, improving reasoning step evaluation by incorporating error type information.
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An Image Transformer pre-trained via self-supervision on document images for improved representation learning.
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An encoder-decoder architecture that reconstructs input images by masking random patches and leveraging a high proportion of masking for self-supervision.
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ColBERTv2 improves late-interaction retrieval by combining aggressive residual compression with denoised supervision to enhance quality and reduce storage.
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Shows that language models can learn diverse language tasks without explicit supervision.
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This paper integrates knowledge distillation with self-supervised contrastive learning to improve student model performance without requiring labeled data.
Sanjiban Choudhury
Proposes Agent Process Reward Models (AgentPRM) for fine-grained step-level supervision as a tractable alternative to large-scale RL for LLM agents.
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This paper investigates the development of process reward models for mathematical reasoning, revealing that their effectiveness depends critically on the granularity and quality of supervision signals.