Support Custom MaxText model (with vLLM engine) in RL rollouts.

[NicoGrande]

Description

This PR finishes the work started by @gagika in #2767. Credits to @gagika for helping with this feature!

This PR adds the changes required to train_rl.py as well as other modules related to Tunix integration to allow for additional configurations needed for the MaxText on vLLM flow to be passed to Tunix.

More specifically, this PR adds vllm_additional_config and vllm_hf_config_path as new arguments such that these values can be pipelined to Tunix for RL.

Additionally, this PR makes some small modifications to tunix_adapter.py to allow for no-ops to be used as mappings when running RL using MaxText for vLLM.

Tests

Gemma3-4B:

Local (v6e-4 VM):

NEW_MODEL_DESIGN=True  HF_TOKEN=$HF_TOKEN TPU_BACKEND_TYPE=jax python3 -m src.MaxText.rl.train_rl src/MaxText/configs/rl.yml \
  model_name=gemma3-4b \
  tokenizer_path=google/gemma-3-4b-it \
  run_name=$WORKLOAD \
  base_output_directory=$OUTPUT_PATH \
  hf_access_token=$HF_TOKEN \
scan_layers=False \
load_parameters_path="gs://maxtext-gemma/unified/gemma3/4b/unscanned/2025-08-09-01-17/0/items" \
  vllm_hf_config_path=src/MaxText/integration/vllm/maxtext_vllm_adapter   vllm_additional_config='{"maxtext_config": {"model_name": "gemma3-4b", "max_prefill_predict_length": 28, "max_target_length": 32, "ici_tensor_parallelism": 4}}'

Output: logs

Checklist

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