EP-GRPO: Entropy-Progress Aligned Group Relative Policy Optimization with Implicit Process Guidance

• URL: http://arxiv.org/abs/2605.04960v1
• Date: Wed, 06 May 2026 14:21:54 GMT
• Title: EP-GRPO: Entropy-Progress Aligned Group Relative Policy Optimization with Implicit Process Guidance
• Abstract summary: We propose Entropy-Progress Aligned GRPO, a framework that mines the model's intrinsic information flow for dense, self-supervised guidance.<n>EP- GRPO achieves superior accuracy and efficiency compared to GRPO and its variants.
• Score: 3.463914032107119
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning with verifiable rewards (RLVR), particularly Group Relative Policy Optimization (GRPO), has advanced LLM reasoning. However, GRPO suffers from three credit assignment failures: uniform token-level granularity that ignores heterogeneous informational value, uniform polarity that penalizes correct steps and rewards incorrect ones, and zero-variance collapse that erases outcome-driven gradients. We systematically quantify these failures, revealing highly non-uniform token informativeness, widespread step-level polarity misalignment, and substantial training waste. To address these limitations, we propose Entropy-Progress Aligned GRPO (EP-GRPO), a framework that mines the model's intrinsic information flow for dense, self-supervised guidance. EP-GRPO integrates entropy-gated modulation to prioritize high entropy decision pivots, implicit process signals from policy divergence anchored to outcome advantages for directional token-level feedback without external reward models, and cumulative entropy mapping that enables progress-aligned advantage normalization, naturally maintaining gradient flow under zero reward variance. Extensive experiments on mathematical reasoning benchmarks demonstrate that EP-GRPO achieves superior accuracy and efficiency compared to GRPO and its variants. The code will be available.

Related papers

• ACPO: Adaptive Credit Policy Optimization via Fine-Grained Surrogate Entropy [45.28312869386833]
  We propose a token-level credit assignment framework based on a mode-local surrogate entropy.<n>ACPO asymmetrically modulates policy updates by emphasizing uncertain decisions in successful rollouts and overconfident tokens in failed rollouts.<n>Experiments on mathematical reasoning and coding benchmarks, including AIME 2025 and HumanEvalPro, show that ACPO consistently improves over strong RL baselines.
  arXiv  Detail & Related papers  (2026-07-03T09:14:27Z)
• Stabilizing On-Policy Distillation for MLLM Reasoning with Global Normalization [9.860371691501076]
  On-policy distillation (OPD) has recently emerged as an important post-training paradigm.<n>We propose Globally Normalized Distillation Policy Optimization (GNDPO), a practical method that stabilizes optimization by transforming raw KL scores into batch-level relative advantages.
  arXiv  Detail & Related papers  (2026-06-08T06:41:31Z)
• One-Way Policy Optimization for Self-Evolving LLMs [63.8638342097375]
  Reinforcement Learning with Verifiable Rewards (RLVR) has become a promising paradigm for scaling reasoning capabilities of Large Language Models (LLMs)<n>We propose One-Way Policy Optimization (OWPO), a method based on the principle of decoupling optimization direction from update magnitude.<n> Experimental results demonstrate that OWPO outperforms strong baselines, including DAPO, OPD, and MOPD.
  arXiv  Detail & Related papers  (2026-05-21T08:25:27Z)
• Entropy Polarity in Reinforcement Fine-Tuning: Direction, Asymmetry, and Control [77.8471519867791]
  Empirically, we show that entropy polarity reliably predicts entropy changes.<n>We propose Polarity-Aware Policy Optimization (PAPO), which preserves both polarity branches and implements entropy control through advantage reweighting.
  arXiv  Detail & Related papers  (2026-05-12T08:47:05Z)
• Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data [55.84428098924793]
  We propose Constrained Uniform Top-K Sampling (CUTS), a parameter-free decoding strategy enforcing structure-preserving exploration.<n>We integrate this into Mixed-CUTS, a training framework synergizing exploitative and exploratory rollouts to amplify intra-group advantage variance.<n> Notably, Mixed-CUTS improves Pass@1 accuracy on the challenging AIME25 benchmark by up to 15.1% over standard GRPO.
  arXiv  Detail & Related papers  (2026-04-20T16:43:28Z)
• ERPO: Token-Level Entropy-Regulated Policy Optimization for Large Reasoning Models [3.463914032107119]
  We show that Group Relative Policy Optimization (GRPO) typically assigns a uniform, sequence-level advantage to all tokens.<n>We propose Entropy-Regulated Policy Optimization (ERPO), which transitions the optimization focus from coarse sequences to fine-grained token dynamics.
  arXiv  Detail & Related papers  (2026-03-30T09:20:25Z)
• Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization [60.87651283510059]
  Group Relative Policy Optimization (GRPO) effectively scales LLM reasoning but incurs prohibitive computational costs.<n>We propose Dynamic Pruning Policy Optimization (DPPO), a framework that enables dynamic pruning while preserving unbiased gradient estimation.<n>To mitigate the data sparsity induced by pruning, we introduce Dense Prompt Packing, a window-based greedy strategy.
  arXiv  Detail & Related papers  (2026-03-04T14:48:53Z)
• WS-GRPO: Weakly-Supervised Group-Relative Policy Optimization for Rollout-Efficient Reasoning [67.45237332694025]
  Group Relative Policy Optimization is effective for training language models on complex reasoning.<n>We propose Weakly Supervised GRPO, which improves rollout efficiency by converting terminal rewards into correctness-aware guidance.
  arXiv  Detail & Related papers  (2026-02-19T02:43:35Z)
• iGRPO: Self-Feedback-Driven LLM Reasoning [88.83313431248473]
  Large Language Models (LLMs) have shown promise in solving complex mathematical problems, yet they still fall short of producing accurate and consistent solutions.<n>We introduce Iterative Group Relative Policy Optimization (iGRPO), a two-stage extension of GRPO that adds dynamic self-conditioning through model-generated drafts.<n>Under matched rollout budgets, iGRPO consistently outperforms GRPO across base models.
  arXiv  Detail & Related papers  (2026-02-09T18:45:11Z)
• Don't Waste Mistakes: Leveraging Negative RL-Groups via Confidence Reweighting [24.822152032771736]
  Reinforcement learning with verifiable rewards (RLVR) has become a standard recipe for improving large language models.<n>We show that negative groups can be leveraged without extra supervision.
  arXiv  Detail & Related papers  (2025-10-09T18:01:44Z)
• CE-GPPO: Coordinating Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning [28.02073546326571]
  Policy entropy reflects the balance between exploration and exploitation during training.<n>Existing methods discard valuable gradient signals from low-probability tokens due to the clipping mechanism.<n>We propose textbfCoordinating textbfEntropy via textbfGradient textbfPreserving textbfPolicy textbfOptimization.
  arXiv  Detail & Related papers  (2025-09-25T03:22:04Z)
• Single-stream Policy Optimization [21.214853668053234]
  We introduce Single-stream Policy Optimization (SPO)<n>SPO replaces per-group baselines with a persistent, KL-adaptive value tracker and normalizes advantages globally across the batch.<n>Being group-free, SPO enables higher throughput and scales effectively in long-horizon or tool-integrated settings.
  arXiv  Detail & Related papers  (2025-09-16T16:39:11Z)
• On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization [52.76330545825083]
  Reinforcement learning (RL) has become popular in enhancing the reasoning capabilities of large language models (LLMs)<n>We identify a previously unrecognized phenomenon we term Lazy Likelihood Displacement (LLD), wherein the likelihood of correct responses marginally increases or even decreases during training.<n>We develop a method called NTHR, which downweights penalties on tokens contributing to the LLD. Unlike prior DPO-based approaches, NTHR takes advantage of GRPO's group-based structure, using correct responses as anchors to identify influential tokens.
  arXiv  Detail & Related papers  (2025-05-24T18:58:51Z)
• Uncertainty-Penalized Direct Preference Optimization [52.387088396044206]
  We develop a pessimistic framework for DPO by introducing preference uncertainty penalization schemes. The penalization serves as a correction to the loss which attenuates the loss gradient for uncertain samples. We show improved overall performance compared to vanilla DPO, as well as better completions on prompts from high-uncertainty chosen/rejected responses.
  arXiv  Detail & Related papers  (2024-10-26T14:24:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.