3 papers
cs.LG2026
PTGPO: Prefix Tree Group Policy Optimization
Ruolin Chen, Yang Li, Jihang Wang +4
Training language agents on long-horizon interactive tasks is challenging because sparse terminal rewards provide weak credit signals for intermediate actions. Existing group-based…
cs.LG2026
KITE: KV-Invariant Transformer Expansion for Efficient Agentic LLM Scaling
Zhiheng Hu, Yixun Wei, Jian Zhou +8
Scaling a language model is not only a question of final quality: the architectural choice determines how much computation is spent during training, prompt processing, and autoregr…
cs.RO2026
TGPO: Trace-Guided Policy Optimization for Robot Task Planning via Verifiable Subgoal Generation
Zhihong Liu, Yang Li, Rengming Huang +4
Robot task planning in real-world environments requires mapping abstract natural language instructions to executable action sequences under long horizons and complex constraints. W…