metadata
license: apache-2.0
base_model: laion/r2egym-nl2bash-stack-bugsseq-fixthink-again
tags:
- reinforcement-learning
- code
- pymethods2test
- r2egym
- rl
- rloo-n
- terminus-structured
language:
- en
pipeline_tag: text-generation
library_name: transformers
rl_pymethods2test-r2egym_terminus-structured
RL-trained Qwen3-8B with structured tool calls (terminus-structured agent).
Training Pipeline
SFT (r2egym+nl2bash+swesmith) → RL mixed dataset (37 steps) → RL full r2egym (55 steps) → RL pymethods2test (156 steps, full epoch)
Key Results
- SWEBench-100 pass@3: 37-42% (depending on eval run)
- Pymethods2test pass@8: 91-97%
- SWEBench in-train eval: up to 14 fully solved at various checkpoints
- Training on test-writing (pymethods) maintains code-editing ability (SWEBench)
Training Details
- Base model: laion/r2egym-nl2bash-stack-bugsseq-fixthink-again
- Agent: terminus-structured (bash, view, edit, create, search tools)
- Algorithm: RLOO-N
- Learning rate: 1e-5
- Context: 32k (24k input + 8k output)
- Framework: BenSkyRL + Harbor (JSC HPC)
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("laion/rl_pymethods2test-r2egym_terminus-structured")
tokenizer = AutoTokenizer.from_pretrained("laion/rl_pymethods2test-r2egym_terminus-structured")