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Diffusion Language Models Know the Answer Before Decoding
Paper • 2508.19982 • Published • 27 -
ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding
Paper • 2512.13586 • Published • 94 -
LSRIF: Logic-Structured Reinforcement Learning for Instruction Following
Paper • 2601.06431 • Published • 12 -
Distribution-Aligned Sequence Distillation for Superior Long-CoT Reasoning
Paper • 2601.09088 • Published • 63
Collections
Discover the best community collections!
Collections including paper arxiv:2603.10165
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AgentConductor: Topology Evolution for Multi-Agent Competition-Level Code Generation
Paper • 2602.17100 • Published • 3 -
GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistant
Paper • 2603.01059 • Published • 1 -
Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
Paper • 2603.00618 • Published -
Heterogeneous Agent Collaborative Reinforcement Learning
Paper • 2603.02604 • Published • 188
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OpenClaw-RL: Train Any Agent Simply by Talking
Paper • 2603.10165 • Published • 139 -
Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights
Paper • 2603.12228 • Published • 12 -
Efficient Memory Management for Large Language Model Serving with PagedAttention
Paper • 2309.06180 • Published • 47 -
1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs
Paper • 2410.16144 • Published • 5
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In-Context Reinforcement Learning for Tool Use in Large Language Models
Paper • 2603.08068 • Published • 41 -
OpenClaw-RL: Train Any Agent Simply by Talking
Paper • 2603.10165 • Published • 139 -
T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning
Paper • 2603.03790 • Published • 121
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Agentic Reasoning for Large Language Models
Paper • 2601.12538 • Published • 202 -
From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence
Paper • 2511.18538 • Published • 304 -
Agent Learning via Early Experience
Paper • 2510.08558 • Published • 275 -
Weak-Driven Learning: How Weak Agents make Strong Agents Stronger
Paper • 2602.08222 • Published • 283
-
Diffusion Language Models Know the Answer Before Decoding
Paper • 2508.19982 • Published • 27 -
ReFusion: A Diffusion Large Language Model with Parallel Autoregressive Decoding
Paper • 2512.13586 • Published • 94 -
LSRIF: Logic-Structured Reinforcement Learning for Instruction Following
Paper • 2601.06431 • Published • 12 -
Distribution-Aligned Sequence Distillation for Superior Long-CoT Reasoning
Paper • 2601.09088 • Published • 63
-
OpenClaw-RL: Train Any Agent Simply by Talking
Paper • 2603.10165 • Published • 139 -
Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights
Paper • 2603.12228 • Published • 12 -
Efficient Memory Management for Large Language Model Serving with PagedAttention
Paper • 2309.06180 • Published • 47 -
1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs
Paper • 2410.16144 • Published • 5
-
In-Context Reinforcement Learning for Tool Use in Large Language Models
Paper • 2603.08068 • Published • 41 -
OpenClaw-RL: Train Any Agent Simply by Talking
Paper • 2603.10165 • Published • 139 -
T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning
Paper • 2603.03790 • Published • 121
-
AgentConductor: Topology Evolution for Multi-Agent Competition-Level Code Generation
Paper • 2602.17100 • Published • 3 -
GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistant
Paper • 2603.01059 • Published • 1 -
Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
Paper • 2603.00618 • Published -
Heterogeneous Agent Collaborative Reinforcement Learning
Paper • 2603.02604 • Published • 188
-
Agentic Reasoning for Large Language Models
Paper • 2601.12538 • Published • 202 -
From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence
Paper • 2511.18538 • Published • 304 -
Agent Learning via Early Experience
Paper • 2510.08558 • Published • 275 -
Weak-Driven Learning: How Weak Agents make Strong Agents Stronger
Paper • 2602.08222 • Published • 283