EmoDistill: Offline Emotion Skill Distillation for Language Model Agents in Adversarial Negotiation

• URL: http://arxiv.org/abs/2605.26785v1
• Date: Tue, 26 May 2026 09:54:53 GMT
• Title: EmoDistill: Offline Emotion Skill Distillation for Language Model Agents in Adversarial Negotiation
• Abstract summary: We introduce textbfEmoDistill, an offline framework for distilling emotional negotiation skills into language model agents.<n>EmoDistill decomposes emotional strategy into emotion selection and emotion expression.<n>Across four emotion-sensitive negotiation domains, SLM policies trained under the EmoDistill framework achieve the highest utility.
• Score: 44.481959726034574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Post-trained LLMs are often optimized to align responses with human preferences, making them safe, polite, and conversationally appropriate. In adversarial negotiation, however, this alignment can become a vulnerability: emotionally framed language may steer agents toward the counterparty's interests. Using GoEmotions-based affective prompting, we show that emotion substantially shifts negotiation outcomes, suggesting that emotion is a strategic action channel rather than a surface style. Thus, we introduce \textbf{EmoDistill}, an offline framework for distilling emotional negotiation skills into language model agents. EmoDistill decomposes emotional strategy into emotion selection and emotion expression: an Implicit Q-Learning (IQL) selector learns \emph{which} emotion to express, while a Low-Rank Adaptation (LoRA)-based policy learns \emph{how} to express it through Supervised Fine-Tuning (SFT) and Judge Policy Optimization (JPO). Across four emotion-sensitive, high-stakes negotiation domains, SLM policies trained under the EmoDistill framework achieve the highest utility, outperforming vanilla SLM/LLM baselines and IQL-only emotion selection. Ablations show that emotion conditioning is essential, and transfer studies demonstrate generalization across domains, unseen counterparties, and trained-vs-trained tournaments. Overall, EmoDistill learns skills from offline agent-to-agent interactions, avoiding costly online negotiation during training.

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