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Fine-grained credit assignment in LLM agents can be unreliable due to downstream randomness, but AdaStep's optimal shrinkage coefficient precisely balances local and global rewards.
Prior claims of SVD-based compression effectiveness crumble under standardized evaluation, revealing that performance varies dramatically across models and setups.
TRACE transforms high-performing LLMs into consistently reliable agents, achieving a remarkable 34.6-point boost in task consistency.
