Learning to Negotiate: Multi-Agent Deliberation for Collective Value Alignment in LLMs
LLM alignment has progressed in single-agent settings through paradigms such as RL with human feedback (RLHF), while recent work explores scalable alternatives such as RL with AI feedback (RLAIF) and dynamic alignment objectives. However, these approaches remain limited in multi-stakeholder settings, where conflicting values arise and deliberative negotiation is required. This work proposes a multi-agent negotiation-based alignment framework that aligns LLMs to Collective Agency (CA)-an existing alignment objective introduced to promote the continual expansion of agency-while simultaneously improving conflict-resolution capability. To enable scalable training, two self-play LLM instances ...
| Authors | Panatchakorn Anantaprayoon, Nataliia Babina, Nima Asgharbeygi et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 March 2026 |
| Last updated at source | 9 April 2026 |
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