Neuro-Symbolic Agentic Reinforcement Learning for Long-Term Original Character Companionship and Interaction (2026.acl-short)
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| Challenge: | Existing LLM-based agents that are optimized by prompting or supervised fine-tuning exhibit a generalization gap in long-horizon, socially rich interactions. |
| Approach: | They propose a framework that formalizes OC companion agents’ interactions as a POMDP and decomposes the agent into three sub-policies optimized via closed-loop RL from AI feedback with verifiable rewards in a graph-constrained action space. |
| Outcome: | The proposed framework formalizes OC companion agents’ interactions as a POMDP and decomposes the agent into three sub-policies (Router, Memory, and Persona) with verifiable rewards in a graph-constrained action space. |
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