MAPO: Mixed Advantage Policy Optimization for Long-Horizon Multi-Turn Dialogue | ScienceToStartup | ScienceToStartup

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References (30)

[1]

EMPA: Evaluating Persona-Aligned Empathy as a Process

2026Shiyao Zhang, Yuhan Zhan et al.

[2]

Kimi K2.5: Visual Agentic Intelligence

2026Kimi Team Yifan Bai, Yifan Bai et al.

[3]

DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

Founder's Pitch

"MAPO is a reinforcement learning algorithm that improves training stability and performance for subjective multi-turn dialogue tasks by leveraging dense process feedback and mixed advantage estimation."

Dialogue Agents•Score: 7•View PDF ↗

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MAPO: Mixed Advantage Policy Optimization for Long-Horizon Multi-Turn Dialogue

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References (30)

Founder's Pitch

"MAPO is a reinforcement learning algorithm that improves training stability and performance for subjective multi-turn dialogue tasks by leveraging dense process feedback and mixed advantage estimation."

Commercial Viability Breakdown

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MAPO: Mixed Advantage Policy Optimization for Long-Horizon Multi-Turn Dialogue

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References (30)

Founder's Pitch

"MAPO is a reinforcement learning algorithm that improves training stability and performance for subjective multi-turn dialogue tasks by leveraging dense process feedback and mixed advantage estimation."

Commercial Viability Breakdown

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