Context-dependent agent evaluation with orthogonal equilibrium learning

arXiv:2609.31897 · cs.AI · Submitted 2026-09-25 · Read on arXiv

cs.AI

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/Farama-Foundation/HighwayEnv

Terminology

• Aggregation — 18× in this paper · explained in 1 episode
• Regularization — 17× in this paper · explained in 3 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Consistency — 11× in this paper · explained in 3 episodes
• F1 Score — 10× in this paper · explained in 3 episodes
• Reinforcement Learning — 8× in this paper · explained in 4 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Nash Equilibrium — 6× in this paper · explained in 2 episodes
• Regret — 6× in this paper · explained in 3 episodes
• Axioms — 5× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Theoretical Guarantees — 4× in this paper · explained in 2 episodes
• AI Alignment — 3× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Accuracy — 3× in this paper · explained in 1 episode
• Pairwise Comparisons — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Influence Functions — 2× in this paper · explained in 2 episodes
• KL Regularization — 2× in this paper · explained in 2 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Q-learning — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Learning Equilibria of Simulation-Based Games
• Offline Two-Player Zero-Sum Markov Games with KL Regularization
• LLM Routing with Dueling Feedback
• Prompt-to-Leaderboard
• AI Alignment From Social Choice Perspectives
• Evaluating Agents using Social Choice Theory
• Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
• EmbeddingGemma: Powerful and Lightweight Text Representations
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
• Beyond Pessimism: Offline Learning in KL-regularized Games

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