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xupy21
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xupy21
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Across 5 long-horizon benchmarks (2 in-distribution agentic coding, 3 out-of-distribution), ContextRL improves over the standard GRPO baseline by +3.2 points on average, while improving every individual benchmark.
| Benchmark | Base | RL (GRPO) | ContextRL (Ours) |
|---|---|---|---|
| SWE-Bench Verified | 26.6 | 28.0 | 30.2 |
| SWE-Bench Lite | 21.0 | 21.7 | 24.0 |
| LiveCodeBench v6 | 21.7 | 22.3 | 24.0 |
| LongBench v2 (Overall) | 27.4 | 27.0 | 29.6 |
| LongBench v2 (Long) | 21.3 | 24.1 | 28.7 |
| NIAH | 68.3 | 65.5 | 71.3 |
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 its Klear-AgentForge-8B base 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.