Trending Research
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
hiyouga/easyr1
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Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-domain generalization and lacking explicit reasoning processes.
Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
Efficiently acquiring external knowledge and up-to-date information is essential for effective reasoning and text generation in large language models (LLMs).
Attentive Reasoning Queries: A Systematic Method for Optimizing Instruction-Following in Large Language Models
We present Attentive Reasoning Queries (ARQs), a novel structured reasoning approach that significantly improves instruction-following in Large Language Models through domain-specialized reasoning blueprints.
Self-rewarding correction for mathematical reasoning
volcengine/verl
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We study self-rewarding reasoning large language models (LLMs), which can simultaneously generate step-by-step reasoning and evaluate the correctness of their outputs during the inference time-without external feedback.
GENERator: A Long-Context Generative Genomic Foundation Model
Recent developments in genomic language models have underscored the potential of LLMs in deciphering DNA sequences.
Retrieval-Augmented Generation with Hierarchical Knowledge
Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks.
OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases.
Evaluating Self-Supervised Learning for Molecular Graph Embeddings
hansen7/molgrapheval
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NeurIPS 2023
Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular graphs due to the staggering number of potential molecules and the high cost of obtaining labels.
From System 1 to System 2: A Survey of Reasoning Large Language Models
Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning.
Aggregated Contextual Transformations for High-Resolution Image Inpainting
zyddnys/manga-image-translator
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For improving texture synthesis, we enhance the discriminator of AOT-GAN by training it with a tailored mask-prediction task.
Ranked #11 on
Image Inpainting
on Places2
