LLMs rely on semantic cues for matrix-game equilibria but can acquire approximate computation via residual training on small instances, with a Lipschitz proof enabling transfer to larger anonymous games.
arXiv preprint arXiv:2510.15414 , year =
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LLMs trained on Codenames via RLVR exhibit scale-dependent effects: the 8B model gains on creativity benchmarks while smaller models gain on reasoning benchmarks.
Agent-World autonomously synthesizes verifiable real-world tasks and uses continuous self-evolution to train 8B and 14B agents that outperform proprietary models on 23 benchmarks.
citing papers explorer
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Equilibrium Residuals Expose Three Regimes of Matrix-Game Strategic Reasoning in Language Models
LLMs rely on semantic cues for matrix-game equilibria but can acquire approximate computation via residual training on small instances, with a Lipschitz proof enabling transfer to larger anonymous games.
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Playing with Words, Improving with Rewards: Training Language Models for Creative Association
LLMs trained on Codenames via RLVR exhibit scale-dependent effects: the 8B model gains on creativity benchmarks while smaller models gain on reasoning benchmarks.
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Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Agent-World autonomously synthesizes verifiable real-world tasks and uses continuous self-evolution to train 8B and 14B agents that outperform proprietary models on 23 benchmarks.