The authors define a strategy as a group of reasoning trajectories that respond coherently to policy updates, rather than as a particular written solution.
Preserving multiple reasoning strategies prevents late-stage collapse in RLVR
The authors connect abrupt performance loss to strategy concentration, derive an early-warning metric and test a strategy-balancing training method.
Top University
Qiyuan Huang · Tianshi Xu · Meng Li
Peking University
Research Digest··3 min read
Thread:Test-Time Scaling Dynamics
Huang, Xu and Li study why language models trained with reinforcement learning from verifiable rewards can improve initially, then suffer abrupt accuracy declines.
Why this paper
From Peking University
In one line
Reinforcement learning with verifiable rewards collapses because optimization concentrates probability onto one strategy, while accuracy requires multiple; Mesh Learning prevents this.
What we could check
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- ·No stated limitations found
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