ORPO performs preference alignment during supervised fine-tuning via a monolithic odds ratio penalty, allowing 7B models to outperform larger state-of-the-art models on alignment benchmarks.
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Agent Island is a new multiagent game environment that functions as a dynamic benchmark resistant to saturation and contamination, with Bayesian ranking showing OpenAI GPT-5.5 as the strongest performer among 49 models across 999 games.
Introduces a pointwise total-variation robust Plackett-Luce loss for listwise preference optimization that exactly decomposes into nominal loss plus a worst-case correction obtained by reverse-sorting implicit scores.
ORM-based test-time verification improves Text-to-SQL accuracy over heuristic selection by up to 4.33% on BIRD and 2.10% on Spider using automated labeling.
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
Risk-sensitive preference games using convex risk measures produce policies that are robust across data strata and match or exceed standard Nash learning performance without added cost.
LLM-driven pairwise comparisons create a partial order on software licenses by permissiveness while leveraging existing taxonomies to identify attributes tied to restrictiveness.
citing papers explorer
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ORPO: Monolithic Preference Optimization without Reference Model
ORPO performs preference alignment during supervised fine-tuning via a monolithic odds ratio penalty, allowing 7B models to outperform larger state-of-the-art models on alignment benchmarks.
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Agent Island: A Saturation- and Contamination-Resistant Benchmark from Multiagent Games
Agent Island is a new multiagent game environment that functions as a dynamic benchmark resistant to saturation and contamination, with Bayesian ranking showing OpenAI GPT-5.5 as the strongest performer among 49 models across 999 games.
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Distributionally Robust Listwise Preference Optimization
Introduces a pointwise total-variation robust Plackett-Luce loss for listwise preference optimization that exactly decomposes into nominal loss plus a worst-case correction obtained by reverse-sorting implicit scores.
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Test-Time Verification for Text-to-SQL via Outcome Reward Models
ORM-based test-time verification improves Text-to-SQL accuracy over heuristic selection by up to 4.33% on BIRD and 2.10% on Spider using automated labeling.
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One Generator, Any Process: LLM-Conditioning for the LHC
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
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Structure from Strategic Interaction & Uncertainty: Risk Sensitive Games for Robust Preference Learning
Risk-sensitive preference games using convex risk measures produce policies that are robust across data strata and match or exceed standard Nash learning performance without added cost.
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Partially ordering software licenses
LLM-driven pairwise comparisons create a partial order on software licenses by permissiveness while leveraging existing taxonomies to identify attributes tied to restrictiveness.