Qwen/Qwen3-4B trained with GRPO on the CollabLLM medium document-writing task using the verl CollabLLM recipe (no SFT warm start). Merged HF checkpoint — load it directly, no adapter step.

Provenance


merged from	`collabllm-qwen3-4B-medium-large-epoch1/global_step_129/actor` (8-shard FSDP)
merged with	`python -m verl.model_merger merge --backend fsdp`
training data	yuhan-nlp/collabllm-medium-rl-grpo

Evaluation

No metrics are reported here on purpose. The benchmark runs backing this checkpoint — per-example traces, judge outputs and summary JSON — live in yuhan-nlp/collabllm-medium-outputs under benchmark_runs/, which is the single source of truth for the numbers.

Setup used there: assistant = this model, user simulator Qwen/Qwen3.5-9B, judge Qwen/Qwen3.5-27B, thinking off everywhere, --user_sim_prompt sim_gap_paper_lazy_full --assistant_max_tokens 4096 --max_new_turns 14, --eval_size 100.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
rid = "yuhan-nlp/verl-grpo-medium-qwen3-4b-step129-repro"
tok = AutoTokenizer.from_pretrained(rid)
model = AutoModelForCausalLM.from_pretrained(rid, dtype="bfloat16", device_map="auto")

The chat template ships as chat_template.jinja (transformers >= 4.57 keeps it out of tokenizer_config.json); AutoTokenizer picks it up automatically.