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cs.LG2026

Benchmarking the Residual: What Long-Horizon Evaluations Add Beyond Matched Short-Task Performance

Chao Peng, Zhiheng Lyu, Peijie Dong +2

The paper proposes a benchmark metric called the horizon residual to compare long-horizon task success against predictions from short-stage baselines, highlighting how performance…

cs.LG2026

LEPO: Latent Reasoning Policy Optimization for Large Language Models

Yuyan Zhou, Jiarui Yu, Hande Dong +4

Recently, latent reasoning has been introduced into large language models (LLMs) to leverage rich information within a continuous space. However, without stochastic sampling, these…

cs.LG2026

Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective

Zhezheng Hao, Hong Wang, Haoyang Liu +6

Reinforcement Learning with Verifiable Rewards (RLVR) serves as a cornerstone technique for enhancing the reasoning capabilities of Large Language Models (LLMs). However, its train…

cs.LG2026

GAPO: Robust Advantage Estimation for Real-World Code LLMs

Jianqing Zhang, Zhezheng Hao, Wei Xia +7

Reinforcement learning (RL) is widely used for post-training large language models (LLMs) in code editing, where group-relative methods, such as GRPO, are popular due to their crit…

cs.LG2025

ReDit: Reward Dithering for Improved LLM Policy Optimization

Chenxing Wei, Jiarui Yu, Ying Tiffany He +3

DeepSeek-R1 has successfully enhanced Large Language Model (LLM) reasoning capabilities through its rule-based reward system. While it's a ''perfect'' reward system that effectivel…