An agentic framework enhanced with an experience memory designed for the sequential setting and addressing common challenges of sequential decision-making such as credit assignment is introduced, and it is shown that post-game reflection and rule extraction yield measurable improvements on tic-tac-toe without modifying...
Self-Play Search Distillation (SPSD), a framework for generating superhuman synthetic data via self-play of MuZero-like networks trained on board games, offers an annotation-efficient way to create high-quality synthetic data for improving LLM performance in reasoning tasks.
In LURE, an LLM evader positions tasks along each environment's difficulty axis to stay one step ahead of a planner-executor pursuer that hunts it down through verifiable interaction, and the unified model attains stronger aggregate OOD zero-shot accuracy than all trained baselines across nine held-out benchmarks from...
While large language models (LLMs) have made notable progress in strategy generation and task planning, they still struggle to promptly detect tactical imbalances and revise strategies dynamically in complex multi-agent environments such as StarCraft II. To address this, we propose Reflection-Augmented Strategy Generat...
The results suggest that broad SFT brings most of the model's capability improvement; turn-local supervision can be effective when failure detection is precise, with observed transfer concentrated primarily within-family.
UnifiedPlayers, a cooperative framework comprising a Planning Player that generates tasks, an Execution Player that produces multi-turn trajectories with Python tool calls, and an Evaluation Player that constructs executable verifiers, highlights cooperation among specialized players as a promising path toward self-enh...
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