aiqbench analysis
EvoCurr introduces a novel inference-time framework where LLMs dynamically generate intermediate tasks and executable policies to solve complex decision-making problems without updating model parameters. By co-evolving curricula based on real-time feasibility checks, the system achieves near-perfect win rates in high-difficulty environments like StarCraft II and LiveCodeBench Hard. This approach demonstrates a significant advancement in adaptive learning strategies for large language models tackling multi-step reasoning challenges.
EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making
arXiv:2508.09586v3 Announce Type: replace Abstract: Complex decision-making often requires agents to progress through intermediate tasks rather than solve the final target directly. Existing LLM self-refinement methods typically iterate on a fixed target, while curriculum-learning methods often rely on hand-designed schedules, training-time optimization, or domain-specific difficulty metrics. To smooth out the learning curve with adaptive curriculum desgin, we introduce EvoCurr, a general inference-time framework that co-evolves curricula and executable policies. A Designer proposes verifiable intermediate tasks from the latest accepted progress and recent failures, while a Solver generates or trains policies for these tasks. A task-policy pair is accepted only when it passes hard feasibility checks and reaches a specified performance threshold meanwhile an accepted-floor constraint preserves the latest verified checkpoint and prevents failed harder attempts from overwriting mastered behavior. We evaluate EvoCurr across code-as-policy and closed-loop MARL settings on StarCraft II super-late-game micromanagement, hardest stress test, where EvoCurr achieves 96.7--99.2% macro-average win rates across three LLM backbones without LLM parameter training. The performance improvments on other experiments, Flatland, POGEMA, Overcooked, and LiveCodeBench Hard, reflect the generality of our EvoCurr.
Abstract:Complex decision-making often requires agents to progress through intermediate tasks rather than solve the final target directly. Existing LLM self-refinement methods typically iterate on a fixed target, while curriculum-learning methods often rely on hand-designed schedules, training-time optimization, or domain-specific difficulty metrics. To smooth out the learning curve with adaptive curriculum desgin, we introduce EvoCurr, a general inference-time framework that co-evolves curricula and executable policies. A Designer proposes verifiable intermediate tasks from the latest accepted progress and recent failures, while a Solver generates or trains policies for these tasks. A task-policy pair is accepted only when it passes hard feasibility checks and reaches a specified performance threshold meanwhile an accepted-floor constraint preserves the latest verified checkpoint and prevents failed harder attempts from overwriting mastered behavior. We evaluate EvoCurr across code-as-policy and closed-loop MARL settings on StarCraft II super-late-game micromanagement, hardest stress test, where EvoCurr achieves 96.7--99.2% macro-average win rates across three LLM backbones without LLM parameter training. The performance improvments on other experiments, Flatland, POGEMA, Overcooked, and LiveCodeBench Hard, reflect the generality of our EvoCurr.
Artificial Intelligence (cs.AI)
arXiv:2508.09586 [cs.AI]
(or arXiv:2508.09586v3 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2508.09586 Focus to learn more arXiv-issued DOI via DataCite
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