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Don't Rush from SFT to RL! Your Multimodal LLM May Be Training 'Injured'

A study reveals that the common SFT→RL pipeline for multimodal LLMs causes performance degradation after SFT due to distributional drift, forcing RL to merely recover lost ground. PRISM introduces an intermediate distribution alignment stage with a mixture-of-experts discriminator to separately correct perceptual and reasoning drifts, achieving significant gains across benchmarks.

Source量子位Author: 衡宇
Don't Rush from SFT to RL! Your Multimodal LLM May Be Training 'Injured'
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Key points and analysis

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Key points

  • SFT causes performance drops in strong multimodal LLMs (e.g., -5.2% for Qwen3-VL-8B)
  • SFT introduces two distinct biases: superficial imitation and entangled perceptual/reasoning drift
  • PRISM uses a MoE discriminator with separate perceptual and reasoning experts for alignment
  • The method provides up to +6.0 average improvement on Qwen3-VL-8B across multiple benchmarks

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