Wenboz

TCOD-v1-OPD-Qwen2.5-3B-WebShop

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README

License: apache-2.0

Training configuration

Table

Framework	trinity-rft (TCOD), verl FSDP backend
Algorithm	on-policy distillation (`multi_turn_opd` advantage, KL coef 1.0)
Optimizer LR	1e-6
Training steps	150 (`save_interval` 50)
batch_size / train_batch_size	16 / 64, repeat_times 1
Max prompt / response (train)	4096 / 512
Env steps per episode (train)	15
Staleness control	`max_staleness=2`, NCCL weight sync every step
Sequence parallel / grad clip	ulysses SP 2, grad_clip 1.0, `max_token_len_per_gpu=16384`
Rollout	vLLM, prefix caching on, CUDA graph on, 8×H100

Usage

Standard chat model; use it inside a WebShop agent loop with the GiGPO/verl-agent prompt template (the <think>...</think> <action>...</action> contract).

python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Wenboz/TCOD-v1-OPD-Qwen2.5-3B-WebShop")
model = AutoModelForCausalLM.from_pretrained("Wenboz/TCOD-v1-OPD-Qwen2.5-3B-WebShop", torch_dtype="bfloat16", device_map="auto")