A new arXiv preprint introduces AdaPrefix-GRPO, a method that addresses GRPO's failure to learn from problems where no rollout succeeds by prepending correct solution prefixes and dynamically adjusting prefix length to maintain ~50% success rate per problem throughout training. The prefix assistance is gradually withdrawn so the final model solves problems unaided. On hard math benchmarks, the method more than doubles GRPO accuracy for a 0.6B model (2.1x) and achieves 1.7x improvement on AIME, while halving trace length, with larger gains on smaller models. The implementation requires only data preparation changes and a loss mask, leaving the trainer unchanged.
Researchers introduce AdvGRPO, a co-training framework that makes GRPO viable for joint attacker-defender optimization in LLM red teaming, addressing previously reported instability. The method uses dense multi-channel rewards and decoupled advantage normalization, with a curriculum progressing from single-turn to multi-turn attacks before bootstrapping co-training. Co-trained defenders outperform baselines on safety benchmarks, and the attacks show transferability across models.
A new arXiv preprint introduces N-GRPO, an exploration strategy for the GRPO reinforcement learning framework that improves solution diversity during rollout by mixing embeddings of anchor tokens with their nearest semantic neighbors rather than using token-level sampling or random noise. The method is evaluated on DeepSeek-R1-Distill-Qwen models of various sizes and shows consistent improvements on math reasoning benchmarks plus out-of-distribution generalization. The work targets a known limitation in RLHF-style training: redundant rollout trajectories that reduce effective learning signal.
Researchers from Carnegie Mellon University introduced Privileged On-Policy Exploration (POPE), a training method that pairs GRPO reinforcement learning with hint-augmented datasets to help LLMs solve hard problems they would otherwise fail to explore. During training, the model receives partial solution prefixes alongside full problems, enabling it to discover complete solutions; it is then trained on both hinted and unhinted versions so it learns to solve problems without hints at inference time. On competition math benchmarks AIME 2025 and HMMT 2025, POPE outperforms standard GRPO and supervised fine-tuning, with HMMT pass@1 improving from 31.0% to 37.8%. The method addresses a core bottleneck in RL training—sparse reward exploration—by decomposing hard problem-solving into finding a good starting state and completing the solution.
A new arXiv preprint proposes replacing local step-correctness signals in process reward models with 'prefix gain' — the improvement in solve-rate induced by conditioning a student model on a given reasoning prefix. The authors train a Prefix Utility Model (PUM) using a pairwise ranking objective and evaluate it across Best-of-N selection, beam search, and RL on mathematical reasoning tasks. PUM shows particular strength when candidate pools are large, search budgets are high, or rule-based rewards are sparse. Code, data, and models are released publicly.
This paper identifies a structural asymmetry in agentic reasoning called the 'Thinking-Acting Gap,' where tool use is attempted in only ~30% of rollouts under standard RL training (GRPO), and all-wrong tool-using subgroups suppress learning signals. The authors propose AXPO (Agent eXplorative Policy Optimization), which fixes the thinking prefix and resamples tool calls for all-wrong subgroups, combined with uncertainty-based prefix selection. Evaluated across nine multimodal benchmarks on Qwen3-VL-Thinking at multiple scales, SFT+AXPO outperforms SFT+GRPO by +1.8pp on both Pass@1 and Pass@4 at 8B, with the 8B model surpassing the 32B baseline on Pass@4 using 4× fewer parameters.
Researchers introduce Zone of Proximal Policy Optimization (ZPPO), a training method inspired by Vygotsky's zone of proximal development that embeds teacher guidance in prompts rather than policy gradients or logit imitation. On hard questions where student rollouts fail, ZPPO constructs Binary Candidate-included Questions (BCQ) and Negative Candidate-included Questions (NCQ) to help the student discriminate correct from incorrect responses, with a replay buffer that recirculates hard questions until mastered. Evaluated on the Qwen3 family (0.8B–9B) with a 27B teacher across a 31-benchmark suite covering VLM, LLM, and video tasks, ZPPO outperforms both distillation and GRPO baselines, with the largest gains at the smallest model scale. The method addresses a known failure mode of RL training where zero-reward rollouts produce no gradient signal.
A new arXiv preprint demonstrates that Group Relative Policy Optimization (GRPO) substantially outperforms supervised fine-tuning (SFT) when adapting LLM-based automatic speech recognition to regulated domains using only synthetic TTS data. GRPO alone reduces word error rate by 40% relative to SFT (36.71% → 22.09%), with an SFT+GRPO combination achieving 45% relative reduction. The authors attribute gains to behavioral changes — improved stopping calibration and better audio-text alignment — rather than representational shifts in early layers.
Qwen researchers introduce Group Sequence Policy Optimization (GSPO), a new RL algorithm designed to address severe training instability and model collapse observed in existing methods like GRPO during extended training runs. The core motivation is enabling stable RL scaling for language models to improve reasoning and problem-solving capabilities with increased compute. The paper targets a known bottleneck in post-training pipelines where instability prevents further performance gains.