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GHPO: Adaptive Guidance for Stable and Efficient LLM Reinforcement Learning
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GHPO: Adaptive Guidance for Stable and Efficient LLM Reinforcement Learning
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Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a powerful paradigm for facilitating the self-improvement of large language models (LLMs), particularly in the domain of complex reasoning tasks. However, prevailing on-policy RL methods often contend with significant training instability and inefficiency. This is primarily due to a capacity-difficulty mismatch, where the complexity of training data frequently outpaces the model's current capabilities, leading to critically sparse reward signals and stalled learning progress. This challenge is particularly acute for smaller, more resource-efficient LLMs. To overcome this, we introduce the Guided Hybrid Policy Optimization (GHPO), a novel difficulty-aware reinforcement learning framework. GHPO dynamically calibrates task difficulty by employing adaptive prompt refinement to provide targeted guidance. This unique approach adaptively balances direct imitation learning for problems currently beyond the model's reach with exploration-based reinforcement learning for more manageable tasks, effectively creating a smooth and optimized learning curriculum. Extensive experiments demonstrate that GHPO achieves an average performance gain of approximately 5% across six challenging mathematics benchmarks, consistently outperforming strong on-policy reinforcement learning and curriculum learning baselines. Further analysis confirms that our framework significantly enhances both training stability and final reasoning performance, thus offering a scalable and efficient solution for developing powerful and robust reasoning models.
Forward citations
Cited by 14 Pith papers
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The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms
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The Generalization Spectrum: A Chromatographic Approach to Evaluating Learning Algorithms
The Generalization Spectrum framework shows RL turns memorization into near-transfer more efficiently than SFT, ICL transfer depends on correspondence, and local gains often fail to expand far-transfer radius.
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EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
EvoTrainer co-evolves LLM policies and training harnesses via empirical feedback to match or exceed human-engineered RL on math reasoning, code generation, and long-horizon software engineering.
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Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective
ConSPO introduces a contrastive sequence-level policy optimization that aligns rollout scores with generation likelihoods via length-normalized log-probabilities and an InfoNCE-style group contrast with curriculum mar...
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Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective
ConSPO is a new contrastive sequence-level policy optimization method that addresses GRPO limitations via length-normalized log-probability scores and InfoNCE-style objectives, outperforming baselines on reasoning benchmarks.
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Span-level Wasserstein distances between hidden-state distributions of correct and incorrect rollouts provide a self-supervised signal to reweight advantages in GRPO, improving fine-grained credit assignment on math a...
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Context Learning for Multi-Agent Discussion
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No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning
ECHO jointly optimizes policy and critic via co-evolution, cascaded rollouts, and saturation-aware shaping to deliver non-stale feedback and higher success in open-world LLM agent RL.
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Don't Tell the Answer, Truly Guide the Reasoning During RL Rollouts
HINT boosts LLM reasoning RL by injecting teacher-generated heuristic hints only on all-failed rollouts, keeping hints out of the policy-optimization prompt, and monitoring guidance quality with a new Affinity metric.
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The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Survey that defines agentic RL for LLMs via POMDPs, introduces a taxonomy of planning/tool-use/memory/reasoning capabilities and domains, and compiles open environments from over 500 papers.
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Hide to Guide: Learning via Semantic Masking
SMEPO applies fine-grained semantic masking to expert guidance in RLVR, turning hard problems into fill-in-the-blank tasks while preserving structure, yielding up to 3.2 point accuracy gains and 4.2x faster training.
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LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance
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