Base model: 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-full_terminus-structured")
tokenizer = AutoTokenizer.from_pretrained("laion/rl_r2egym-full_terminus-structured")