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Feng-Yuan Lu

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#artificial intelligence Preprint Sep 2026

$T^5$: Twin-Critic Training for Token-Level Thoughts in Reinforcement Mid-Training

This work proposes tfour, a twin-critic method that calibrates token-level advantages from a single generated trajectory, and describes optimal mixing under the signal-retention constraint and establishes an upper bound on residual mean-induced drift.

Nan Qiao, Ye-Bin Yang, Wei-Nong Wang et al. · 0 citations
Preprint Aug 2026

Beyond Correctness: Benchmarking and Aligning Response Behaviors in Hybrid-Thinking MLLMs

Experiments on Qwen3-VL-4B and Qwen3-VL-8B show that incorporating pattern-specific penalties into reinforcement learning can mitigate cross-mode misalignment while incurring a marginal task performance trade-off.

Xin-Ming Wang, Wei-Nong Wang, Hong-Ming Yang et al. · 2 citations · ⚡1

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