BOND distills Bayesian posteriors over six opponent priority orderings from a teacher LLM into an 8B student model, achieving Brier score 0.114 on CaSiNo while enabling posterior trajectory audits and outperforming a 70B baseline in calibration.
arXiv preprint arXiv:2002.07788 , year=
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Self-play RL in a takeover auction model shows optimal due diligence is modest and finite, decreasing with cost and competition, while simple general methods outperform specialized ones in large intractable games.
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Distilling Bayesian Belief States into Language Models for Auditable Negotiation
BOND distills Bayesian posteriors over six opponent priority orderings from a teacher LLM into an 8B student model, achieving Brier score 0.114 on CaSiNo while enabling posterior trajectory audits and outperforming a 70B baseline in calibration.
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How Much Due Diligence Before You Bid? Learning in Intractable Takeover Auctions
Self-play RL in a takeover auction model shows optimal due diligence is modest and finite, decreasing with cost and competition, while simple general methods outperform specialized ones in large intractable games.