HASP upgrades textual skills into executable Program Functions that intervene in LLM agent loops at inference, post-training, or self-evolution, delivering 25% gains over ReAct and 30.4% over Search-R1 on reasoning benchmarks.
Reflexion: Language agents with verbal reinforcement learning
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Template collapse is a distinct failure mode in agentic RL invisible to entropy; mutual information proxies diagnose it better and SNR-aware filtering using reward variance improves input-dependent reasoning and task performance across planning, math, navigation, and code tasks.
DAPO introduces decoupled clipping and dynamic sampling for LLM RL, achieving 50 on AIME 2024 with Qwen2.5-32B while fully open-sourcing code, data, and the verl-based training system.
ReflectiChain uses latent trajectory rehearsal and retrospective agentic RL inside an LLM world model to raise average step rewards by 250% and restore supply-chain operability from 13.3% to 88.5% on the Semi-Sim benchmark under extreme shocks.
Agentic RAG embeds agents with reflection, planning, tool use, and collaboration into retrieval pipelines to overcome static RAG limitations, and the survey offers a taxonomy by agent count, control, autonomy, and knowledge representation plus applications and open challenges.
citing papers explorer
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Harnessing LLM Agents with Skill Programs
HASP upgrades textual skills into executable Program Functions that intervene in LLM agent loops at inference, post-training, or self-evolution, delivering 25% gains over ReAct and 30.4% over Search-R1 on reasoning benchmarks.
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RAGEN-2: Reasoning Collapse in Agentic RL
Template collapse is a distinct failure mode in agentic RL invisible to entropy; mutual information proxies diagnose it better and SNR-aware filtering using reward variance improves input-dependent reasoning and task performance across planning, math, navigation, and code tasks.
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DAPO: An Open-Source LLM Reinforcement Learning System at Scale
DAPO introduces decoupled clipping and dynamic sampling for LLM RL, achieving 50 on AIME 2024 with Qwen2.5-32B while fully open-sourcing code, data, and the verl-based training system.
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From Topology to Trajectory: LLM-Driven World Models For Supply Chain Resilience
ReflectiChain uses latent trajectory rehearsal and retrospective agentic RL inside an LLM world model to raise average step rewards by 250% and restore supply-chain operability from 13.3% to 88.5% on the Semi-Sim benchmark under extreme shocks.
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Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
Agentic RAG embeds agents with reflection, planning, tool use, and collaboration into retrieval pipelines to overcome static RAG limitations, and the survey offers a taxonomy by agent count, control, autonomy, and knowledge representation plus applications and open challenges.