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