This is the agentic (long-horizon) model released with the paper Context-Aware RL for Agentic and Multimodal LLMs. It is fine-tuned from Qwen3-8B, a general-purpose model, using ContextRL, a context-aware reinforcement learning method that augments standard GRPO with an auxiliary context-selection objective to improve fine-grained context grounding in long-horizon agent trajectories.

Across 5 long-horizon benchmarks (2 in-distribution agentic coding, 3 out-of-distribution), ContextRL improves over the standard GRPO baseline by +1.5 points on average, while improving every individual benchmark.

Metrics: SWE-Bench Verified/Lite resolve rate (%), LiveCodeBench v6 solve rate (%), LongBench v2 accuracy (%), NIAH mean recall (%). On the long-context tasks (LongBench v2, NIAH) where standard outcome-based GRPO struggles or regresses, ContextRL surpasses both the base model and the RL baseline, demonstrating strong out-of-distribution generalization.

This model follows the same interface as Qwen3-8B and can be loaded with transformers. Training and evaluation code, data construction pipelines, and detailed configurations are available in the repository: 👉 https://github.com/xupy2003/ContextAwareRL Please refer to the repo's README for environment setup, inference scripts, and reproduction instructions.