modelscope / modelscope/ms-swift

Qwen3-Omni GRPO Rollout Generates Infinite Repetitive Characters

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Description

During GRPO fine-tuning of Qwen3-Omni-30B on H200 using Megatron-LM, the model gets stuck in an infinite loop during rollout generation, repeatedly outputting the same character and never stopping.

i try to add vllm_mm_processor_cache_gb 0, but still not work ......

megatron rlhf
--rlhf_type grpo
--model /path/to/Qwen3-Omni-30B-A3B-Instruct
--dataset /path/to/dataset.jsonl#8000
--train_type full
--tensor_model_parallel_size 4
--expert_model_parallel_size 4
--pipeline_model_parallel_size 1
--context_parallel_size 1
--global_batch_size 1024
--micro_batch_size 1
--num_generations 8
--steps_per_generation 1
--max_length 16384
--max_completion_length 2048
--max_pixels 1003520
--lr 1e-6
--min_lr 1e-7
--lr_warmup_fraction 0.05
--bf16 true
--beta 0.00
--epsilon 3e-4
--epsilon_high 4e-4
--loss_type grpo
--importance_sampling_level sequence
--dynamic_sample false
--overlong_filter true
--use_vllm true
--vllm_mode colocate
--vllm_tensor_parallel_size 8
--vllm_gpu_memory_utilization 0.4
--vllm_max_model_len 16384
--vllm_mm_processor_cache_gb 0
--reward_funcs random_reward
--reward_weights 1.0
--external_plugins /path/to/plugin.py
--freeze_vit true
--freeze_aligner true
--freeze_parameters talker code2wav
--attention_backend flash
--sequence_parallel true
--padding_free true
--offload_model true
--use_precision_aware_optimizer
--recompute_granularity selective
--sleep_level 2
--moe_permute_fusion true
--moe_grouped_gemm true
--moe_shared_expert_overlap true
--moe_aux_loss_coeff 1e-3
--vit_gradient_checkpointing false
--save_interval 50
--eval_interval 50
--log_interval 1
--log_completions true
--no_save_optim true
--no_save_rng true
--num_workers 8
--dataset_num_proc 8
--wandb_project megatron_swift
--finetune
--seed 42

Name: vllm
Version: 0.12.1.dev0+g4fd9d6a85.d20251217.cu130
Name: torch
Version: 2.9.1+cu130
Name: ms_swift
Version: 3.12.0.dev0

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reproducing the provided GRPO rollout command with the listed Qwen3-Omni, vLLM, PyTorch, and ms-swift versions, then inspect the rollout-generation logs and logged completions. Trace where repeated characters fail to terminate; done means rollout generation stops normally instead of producing an infinite repetitive sequence.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
machine-learning
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Quiet
Clarity
Needs clarification
Newbie friendliness
45/100

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