Sitao Cheng

The work introduces X-Tree: raw actions are first canonicalized into typed tokens such as type⟨date⟩ or click⟨button⟩, then adjacent pairs are recursively merged under an X-Score that weighs recurrence, span length and the fraction of occurrences in successful episodes, yielding a deterministic reusable experience tree mined from a trajectory pool with zero LLM calls; X-Tree is then integrated into three training settings — offline RL with each node as a training instance under a step-matching reward plus a depth-scaled completion bonus, online RLVR with an adaptive skill bonus that supplies signal while the verifier is silent and anneals as it becomes informative, and on-policy self-distillation where a rendered X-Tree replaces the LLM-written skill bank as the self-teacher's privileged c

The work introduces X-Tree: raw actions are first canonicalized into typed tokens such as type⟨date⟩ or click⟨button⟩, then adjacent pairs are recursively merged under an X-Score that weighs recurrence, span length and the fraction of occurrences in successful episodes, yielding a deterministic reusable experience tree mined from a trajectory pool with zero LLM calls; X-Tree is then integrated into three training settings — offline RL with each node as a training instance under a step-matching reward plus a depth-scaled completion bonus, online RLVR with an adaptive skill bonus that supplies signal while the verifier is silent and anneals as it becomes informative, and on-policy self-distillation where a rendered X-Tree replaces the LLM-written skill bank as the self-teacher's privileged c