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Sparse rewards in multi-objective RL are no longer a bottleneck; a simple density correction unlocks faster, more robust learning.
KAT-Coder-V2.5 outperforms existing models in agentic tool-use, showcasing a new paradigm for autonomous coding agents within executable environments.
Agentic coding models can achieve near-SOTA performance by specializing in distinct coding domains before unifying them via on-policy distillation.
