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Model: laion/rl_r2egym-nl2bash-swesmith-pymethods2test_terminus-structured
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: laion/r2egym-nl2bash-stack-bugsseq-fixthink-again
+tags:
+  - reinforcement-learning
+  - code
+  - r2egym,pymethods2test,swesmith,nl2bash
+  - rl
+  - rloo-n
+  - terminus-structured
+language:
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# rl_r2egym-nl2bash-swesmith-pymethods2test_terminus-structured
+
+RL-trained Qwen3-8B with structured tool calls.
+
+Training pipeline: SFT (r2egym+nl2bash+swesmith) → RL mixed dataset (37 steps) → RL full r2egym (55 steps) → RL pymethods2test (110 steps).
+
+Key results:
+- SWEBench-100: 42% pass@3 (vs 37% base with terminus-2)
+- Pymethods2test: 94-100% pass@8
+- 14 SWEBench tasks solved that base model cannot
+- Trained with terminus-structured agent (bash, view, edit, create, search tools)
+
+## Training Details
+
+- **Base model**: [laion/r2egym-nl2bash-stack-bugsseq-fixthink-again](https://huggingface.co/laion/r2egym-nl2bash-stack-bugsseq-fixthink-again)
+- **Training method**: rloo-n with terminus-structured agent (structured tool calls: bash, view, edit, create, search)
+- **Framework**: BenSkyRL + Harbor 
+- **Context**: 32k (24k input + 8k output)
+- **Learning rate**: 1e-5
+
+## SWEBench-Verified Results (100 tasks, pass@3)
+
+| Model | SWEBench pass@3 |
+|---|---|
+| Base SFT (terminus-2) | 37% |
+| This model (terminus-structured) | See eval results |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("laion/rl_r2egym-nl2bash-swesmith-pymethods2test_terminus-structured")
+tokenizer = AutoTokenizer.from_pretrained("laion/rl_r2egym-nl2bash-swesmith-pymethods2test_terminus-structured")
+```