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LLM agents execute tasks through repeated model calls over evolving histories of reasoning, actions, and tool feedback. Aggregate speculative-decoding metrics merge these distinct decisions, providing limited guidance on where draft supervision is needed. We present TraceSpec, a framework connecting trajectory evaluati...
TreeSpark is introduced, a unified training and inference framework extending the semi-autoregressive DSpark architecture that constructs parent-conditioned candidate trees from one backbone pass and replays their expansion, pruning, and budgeted packing during training.
This paper systematically analyzes failures encountered during large-scale RL training on the Huawei Ascend platform, summarizes representative failure types, and identifies three model-side factors relevant to fault reproduction.
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