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Self-summarization in LLMs can enhance reasoning coherence and reduce context exhaustion, leading to a 4% performance boost with shorter rollouts.
Shifting credit assignment to fine-grained decision points boosts agentic RL performance by nearly 4 points, challenging the conventional focus on tool-call boundaries.
Bootstrapping LLM agents to co-evolve as both agent and environment can lead to significant performance gains, with an average improvement of over 4% on complex tasks.
Forget maps: LLMs can learn end-to-end transit route planning directly from data, even grounding GPS coordinates without explicit mapping.
LLM agents can now learn from *everyone's* experience, not just their own, leading to system-wide improvements without requiring additional user effort.
