The authors adapt group-relative REINFORCE to continuous flow language models (CLMs), then introduce pathwise gradients via a critic that predicts expected verifier outcome from intermediate denoising states.
Backpropagating verifier rewards improves reasoning in continuous flow language models
A critic trained on intermediate token embeddings enables pathwise gradient-based reinforcement learning for continuous language models.
Academic
Xu Han · Jiajing Hu · Li-Ping Liu
Tufts University
Research Digest··3 min read
The authors propose CFRL, a post-training framework for continuous flow language models (CLMs) that denoise token embeddings over multiple steps.
Why this paper
From Tufts University
In one line
A critic predicting per-token values lets continuous flow language models improve reasoning through backpropagated verifier rewards.
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