Dual-Adaptive Z-Score PPO (PPO-DAZ3) is an adaptive-clipping variant of Proximal Policy Optimization that replaces the fixed clipping threshold with sample-dependent thresholds derived from action confidence and normalized advantage magnitude. The method retains the rest of the standard PPO training pipeline and is evaluated on three classic control benchmarks. Results show that PPO-DAZ3 improves learning on LunarLander-v3, while a smooth advantage-based variant (PPO-AZ3S) performs best on Acrobot-v1 and standard PPO remains competitive on CartPole-v1, indicating adaptive clipping benefits harder control tasks.
arXiv:2511. 02577v2 Announce Type: replace Abstract: Proximal Policy Optimization (PPO) is widely regarded as one of the most successful deep reinforcement learning algorithms, known for its robustness and effectiveness across a range of problems.
Group Adaptive Clipping Policy Optimization (GAPO) is a plug‑in modification to GRPO methods that adapts the importance‑sampling clipping boundary based on rollout advantage. By allowing rollouts with larger learning signals to receive proportionally greater update headroom, GAPO addresses the limitation of fixed clipping that suppresses rare but informative rollouts. Experiments on Qwen and Llama models show that GAPO consistently improves Pass@1 and Pass@k on math reasoning and coding benchmarks where base model pass rates are low.
arXiv:2606. 23932v1 Announce Type: new Abstract: Proximal Policy Optimization (PPO) is the standard policy-gradient algorithm for on-policy reinforcement learning.
arXiv:2609.39634v1 Announce Type: cross Abstract: Common policy improvement methods, including TRPO, PPO, and GRPO, estimate policy improvement under the behavioral policy's state-visitation distribu...
arXiv:2609.36816v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a cornerstone for improving the reasoning capabilities of large language models (LLMs), but the need for on-poli...
arXiv:2607. 18163v1 Announce Type: cross Abstract: PPO and the GRPO baseline studied here use clipped surrogate objectives whose favorable-direction saturation introduces an abrupt change in the scalar objective's derivative.
arXiv:2603. 03955v2 Announce Type: replace Abstract: Post-training with reinforcement learning (RL) has recently shown strong promise for advancing multimodal agents beyond supervised imitation.
arXiv:2610. 10273v1 Announce Type: new Abstract: Despite its widespread use, Proximal Policy Optimization with clipping (PPO-Clip) remains difficult to tune, and the interactions among critic learning, clipping, and rollout reuse remain incompletely understood.
Optimistic Policy Regularization (OPR) is a simple reward‑shaping technique that augments on‑policy agents with a closed‑form bonus derived from elite action priors estimated from top‑quartile episodes. By adding a bounded transform of the log‑ratio of this prior to the policy’s action distribution, OPR requires no extra networks or off‑policy corrections and can be implemented as a one‑line change to PPO’s reward. Experiments on 49 Atari games show that OPR achieves a median human‑normalized score of 148.4 % with only 10 M steps, outperforming published PPO on 36 of 49 games and surpassing self‑imitation learning when matched on frames and backbone.
The paper introduces Generalized Proximal Policy Optimization (GPPO), a unified framework that extends PPO by incorporating $f$-divergence trust-region theory and novel estimator topologies for large language model reinforcement learning. GPPO demonstrates that different divergences produce distinct policy update dynamics and that estimator design significantly impacts performance. Experiments on eight math and coding benchmarks show GPPO surpasses standard GRPO by up to 50% in math and 14% in coding, achieving state‑of‑the‑art results.
arXiv:2607. 08925v1 Announce Type: new Abstract: Training reinforcement-learning agents directly on physical robots makes every fall costly, since a fall can damage the platform and cannot be undone like a simulator reset; the goal is therefore to minimize falls during training rather than trade them off against return, as constrained Markov decision process (MDP) formulations do.
arXiv:2609.40149v1 Announce Type: new Abstract: Policy regularization in offline reinforcement learning balances policy improvement against reliance on uncertain value estimates. This balance can dif...