3 related articles

Explore how POMDP remodels low-resource machine translation for Bengali, combining MBR decoding and active disambiguation to tackle ambiguity, code-mixing, and speech noise.

Analysis of when POMDP modeling fits care escalation decisions: hidden states, noisy observations, and asymmetric costs. From threshold methods to belief state updates, a phased pragmatic roadmap.

A deep dive into RL for AI agents: from RLHF to Agentic RL, covering PPO vs. GRPO, sparse rewards, tool-calling optimization, and verifiable rewards.