2 papers
cs.LG2026
When LLM Reward Design Fails: Diagnostic-Driven Refinement for Sparse Structured RL
Youting Wang, Yuan Tang, Bowen Liu +2
For sparse, structured reinforcement-learning tasks with semantic reward-function interfaces, LLM-generated reward shaping is better framed as debugging than one-shot generation. W…
cs.MA2026
Pythia: Exploiting Workflow Predictability for Efficient Agent-Native LLM Serving
Shan Yu, Junyi Shu, Yuanjiang Ni +14
As LLM applications grow more complex, developers are increasingly adopting multi-agent architectures to decompose workflows into specialized, collaborative components, introducing…