Thinking-Free Policy Initialization Makes Distilled Reasoning Models
  More Effective and Efficient Reasoners
Authors: name: Xin Xu, name: Cliveb AI, name: Kai Yang, name: Tianhao Chen, name: Yang Wang, name: Saiyong Yang, name: Can Yang
Organization: Tencent

Reinforcement Learning with Verifiable Reward (RLVR) effectively solves
complex tasks but demands extremely long context lengths during training,
leading to substantial computational costs. While multi-stage training can
partially mitigate this, starting with overly short contexts often causes
irreversible performance degradation, ultimately failing to reduce overall
training compute significantly. In this paper, we introduce
**T**hinking-**F**ree **P**olicy **I**nitialization (**TFPI**), a simple yet
effective adaptation to RLVR that bridges long Chain-of-Thought (CoT)
distillation and standard RLVR. TFPI employs a simple *ThinkFree* operation,
explicitly discarding the thinking content via a direct *</think>* append, to
reduce token usage during inference. Training with *ThinkFree*-adapted inputs
improves performance and lowers token consumption, even in the original
slow-thinking mode. Extensive experiments across various benchmarks have shown
that TFPI accelerates RL convergence, achieves a higher performance ceiling,
and yields more token-efficient reasoning models without specialized rewards or
complex training designs. With TFPI only, we train a 4B model to reach 89.0%
accuracy on AIME24 and 65.5% on LiveCodeBench using less than 4K H20 hours.