GLARE: Generative Learning via Adversarial Reward Estimation For Social Dynamics Forecasting

• URL: http://arxiv.org/abs/2609.12165v1
• Date: Thu, 10 Sep 2026 19:52:34 GMT
• Title: GLARE: Generative Learning via Adversarial Reward Estimation For Social Dynamics Forecasting
• Abstract summary: We introduce the Meeting Dynamic Forecasting Benchmark (MDFB)<n>It was constructed from 2,207 real-world meetings and 24,794 future-facing queries.<n>We also present GLARE, an adaptation of adversarial imitation learning to conditional language generation.
• Score: 64.66483488975663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Meeting continuation requires tracking the agenda, speaker roles, participant intentions, and disagreement across long multi-party discussions. We introduce the Meeting Dynamic Forecasting Benchmark (MDFB), constructed from 2,207 real-world meetings and 24,794 future-facing queries. Given a transcript prefix and an active question, a model generates a plausible multi-turn continuation in one call. We evaluate utility---progress toward the question---and human-likeness---plausible conversational flow and role consistency---without requiring exact reproduction of the observed future. We further present GLARE, an adaptation of adversarial imitation learning to conditional language generation. A discriminator ranks the observed continuation above samples from the current actor, and its score supplies a KL-regularized policy reward; retraining on current-policy negatives allows the reward landscape to evolve with the actor. GLARE attains average human-evaluated win rates of 0.66 on utility and 0.70 on human-likeness, outperforming SFT and SPIN while remaining below the observed human continuation. We also demonstrate MDFB as a social reasoning arena for comparing general-purpose models, including closed-source systems, through reference-assisted judgments. Together, these studies illustrate the benchmark's use for both task-specific learning and output-based evaluation of meeting behavior.

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