Papers with exploration-exploitation trade-off
Taming Extreme Tokens: Covariance-Aware GRPO with Gaussian-Kernel Advantage Reweighting (2026.acl-short)
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| Challenge: | Excessive exploitation can cause the model to become overconfident in its suboptimal solutions, thereby limiting its capabilities to explore novel reasoning strategies. |
| Approach: | They propose a method that dynamically down-weights extreme token-level updates via a Gaussian kernel and reduces the instability caused by the trade-off. |
| Outcome: | The proposed method improves downstream performance across reasoning benchmarks and stabilizes entropy as training progresses. |
Unlocking Exploration in RLVR: Uncertainty-aware Advantage Shaping for Deeper Reasoning (2026.findings-acl)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) has shown significant promise for enhancing the reasoning capabilities of large language models (LLMs). |
| Approach: | They propose a model-free method that refines credit assignment by leveraging the model's internal uncertainty signals. |
| Outcome: | Extensive experiments on five mathematical reasoning benchmarks show that the proposed method outperforms strong RLVR baselines on multiple model scales, including 1.5B and 7B. |
CE-GPPO: Coordinating Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing methods for proximal policy optimization discard valuable gradient signals from low-probability tokens due to the clipping mechanism. |
| Approach: | They propose an algorithm that reintroduces gradients from clipped tokens in native PPO in a gentle and bounded manner. |
| Outcome: | The proposed algorithm outperforms strong baselines on reasoning benchmarks on different model scales. |