Part of the Meta-Harness v1b study — co-evolution GRPO (verl) where a shared-weight policy plays two roles: TA (grader / solver, single-turn) and Proposer (harness/scaffold editor via multi-turn SEARCH/REPLACE). Question: does co-evolving the policy and its evaluation harness help, and through which lever (policy vs scaffold)?


Domain	IMO grading
Architecture	Qwen3_5ForCausalLM — 32 layers, hidden 4096
Precision	bf16 (merged from FSDP via `verl.model_merger`)
Notes	Closed-loop co-evolution (evolved harness fed back within the loop).

Collection: https://huggingface.co/collections/hyunseoki/meta-harness-v1b-policy-x-harness-co-evolution-6a5c9877f74920e8922808bc

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("hyunseoki/mh-v1b-imo-9b-closed")
model = AutoModelForCausalLM.from_pretrained("hyunseoki/mh-v1b-imo-9b-closed", torch_dtype="bfloat16")