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The best single-game performer was not the most efficient learner, so one-shot benchmark scores can mislead when choosing an agent for long-horizon work. Teams building self-improving systems should track held-out learning curves against compute cost and verify that saved tools and notes are actually reused. The frozen-weights design makes the results directly relevant to anyone running agents under a fixed budget.

rohanpaul_ai@rohanpaul_aiSource

The research reveals that the best-performing models aren't always the most efficient learners, challenging assumptions about AI self-improvement. Developers and researchers can use agent plasticity metrics to optimize AI training investments, particularly for complex tasks requiring artifact reuse. The findings have practical implications for resource allocation in AI development across industries.

elvis@omarsar0Source

The result argues verifier quality, not sample count, is the real bottleneck in agentic terminal tasks, which reshapes how teams should budget inference tokens. Builders of coding agents and shell automation can adopt the Mid-Harness pattern without touching their existing generator or harness. The comparison between a large GPT-5.6 Sol verifier and a distilled TMAX-9B self-verifier gives practitioners a concrete cost-quality trade-off to replicate.

DAIR.AI@dair_ai11 sourcesSource

This reveals a critical flaw in how LLMs handle dynamic information, potentially causing errors in real-world applications where preferences change frequently. Developers building agents that track changing data should explicitly include current state in prompts rather than relying on models to parse through context history.

rohanpaul_ai@rohanpaul_aiSource

Rubric-augmented rewards trade raw benchmark score for trajectories a judge rates far higher, a hint that outcome-only RL over-optimises for narrow metrics while behavioural rubrics shape more genuinely skilled agents. Teams training terminal agents can reuse the open pipeline to convert existing Agent Skills corpora into RL environments instead of hand-crafting tasks. The US$90,000 generation cost also sets a concrete budget baseline for anyone planning similar corpus builds.

DAIR.AI@dair_ai9 sourcesSource

The method improves RL performance significantly on Qwen models, relevant for developers working with reinforcement learning in ASEAN.

elvis@omarsar0Source