rl_r2egym-nl2bash-swesmith-pymethods2test_terminus-structured – AI Model by laion | AlphaNeural AI

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laion/
rl_r2egym-nl2bash-swesmith-pymethods2test_terminus-structured

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text-generation

reinforcement-learning

r2egym,pymethods2test,swesmith,nl2bash

terminus-structured

laion/r2egym-nl2bash-stack-bugsseq-fixthink-again

text-generation-inference

endpoints_compatible

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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
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")