Tue Le

Minh V. T. Thai, Tue Le, Dung Nguyen Manh et al.

Existing benchmarks for AI coding agents focus on isolated, single-issue tasks such as fixing a bug or adding a small feature. However, real-world software engineering is a long-horizon endeavor: developers interpret high-level requirements, coordinate changes across many files, and evolve codebases over multiple iterations while preserving functionality. We introduce SWE-EVO, a benchmark for this long-horizon software evolution challenge. Constructed from release notes of seven mature open-source Python projects, SWE-EVO comprises 48 tasks requiring multi-step modifications spanning an average of 21 files, validated against test suites averaging 874 tests per instance. Experiments reveal a striking capability gap: GPT-5.4 with OpenHands achieves only 25% on SWE-EVO versus 72.80% achieved by GPT-5.2 on SWE-Bench Verified, showing that current agents struggle with sustained, multi-file reasoning. We also propose Fix Rate, a metric capturing partial progress on these complex, long-horizon tasks.

Tue Le, Nghi D. Q. Bui, Linh Ngo Van et al.

Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful approach for strengthening the reasoning capabilities of large language models (LLMs). Among existing algorithms, Group Relative Policy Optimization (GRPO) has demonstrated strong performance, yet it suffers from a critical issue: low-probability tokens disproportionately dominate gradient updates due to their inherently large gradient magnitudes. This imbalance leads to unstable training and suppresses the contribution of high-probability tokens that are more reliable for learning. In this work, we introduce Token-Regulated Group Relative Policy Optimization (TR-GRPO), a simple yet effective extension of GRPO that assigns token-level weights positively correlated with the model's predicted probability. By downweighting low-probability tokens and emphasizing high-probability ones, TR-GRPO mitigates gradient over-amplification while preserving informative learning signals. Extensive experiments demonstrate that TR-GRPO consistently outperforms GRPO across RLVR tasks, including logic, math, and agentic reasoning, highlighting the importance of regulating token contributions during RL training and establishing TR-GRPO as a robust framework for enhancing LLM reasoning.