Reinforcement learning with verifiable rewards (RLVR) typically evaluates only final outcomes, providing limited learning signal about whether the generated reasoning is consistent with the correct answer. As a result, even when ground-truth answers are available during training, on-policy rollouts can repeatedly produce reasoning that is inconsistent with the answer.We propose Answer-Guided Group Relative Policy Optimization (AG-GRPO) for masked diffusion language models (dLLMs), which generate text through iterative masked-token restoration. AG-GRPO combines standard answer-free (AF) rollouts, sampled without access to the ground-truth answer, with answer-guided (AG) rollouts. In AG rollouts, the model generates reasoning conditioned on an anchored ground-truth answer suffix, and then re-predicts the answer from the generated reasoning for reward computation. We compute group-relative advantages over the combined AF/AG rollout set, allowing answer-guided training signals to improve the answer-free policy used at test time.Across mathematics, puzzle-solving, and code-generation benchmarks, AG-GRPO consistently improves over the pretrained dLLM and prior RL method for masked dLLMs. We further analyze optimization dynamics to study how shared group-relative advantages support signal transfer and affect convergence. Our code is available at https://github.com/JuHyng/ag_grpo.

Autoregressive decoding in large language models (LLMs) necessitates a full forward pass for each generated token, significantly increasing inference latency. To address this limitation, we propose Fractal-LLM, a lossless self-speculative decoding method that embeds a compressed model within selected decoder layers of the original model. Specifically, our approach generates multiple draft tokens in parallel by injecting compressed layers into selected decoder layers. These draft tokens are subsequently verified through a single forward pass of the original model, ensuring the final outputs exactly match those produced by the original model. Experimental results across diverse benchmarks—including GSM8K, XSUM, CNN/DailyMail, and HumanEval—demonstrate that our method achieves substantial inference speed-ups (up to 2.47×) compared to standard autoregressive decoding, without requiring any additional training.