This survey introduces the Generate-Filter-Control-Replay (GFCR) taxonomy to structure rollout pipelines for RL-based post-training of reasoning LLMs.
Fromdata-centrictosample- centric: Enhancing llm reasoning via progressive optimization
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SHIFT selects compact RLVR training subsets using the magnitude of hidden-state change from a single inference rollout plus quality-weighted farthest-first coverage, outperforming training-free baselines on math reasoning and medical QA under low budgets.
SCALER creates adaptive synthetic environments for RL-based LLM reasoning training that outperforms fixed-dataset baselines with more stable long-term progress.
citing papers explorer
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Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning
This survey introduces the Generate-Filter-Control-Replay (GFCR) taxonomy to structure rollout pipelines for RL-based post-training of reasoning LLMs.
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Single-Rollout Hidden-State Dynamics for Training-Free RLVR Data Selection
SHIFT selects compact RLVR training subsets using the magnitude of hidden-state change from a single inference rollout plus quality-weighted farthest-first coverage, outperforming training-free baselines on math reasoning and medical QA under low budgets.
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SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning
SCALER creates adaptive synthetic environments for RL-based LLM reasoning training that outperforms fixed-dataset baselines with more stable long-term progress.