Support add / remove lora adapter

README.md

@@ -32,6 +32,7 @@ Trinity-RFT provides functionalities for users with different backgrounds and ob
 
 ## 🚀 News
 
+* [2026-01] [[Release Notes]](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.4.1) Trinity-RFT v0.4.1 released: upgraded verl to v0.7.0, Tinker backend supports OpenAI API, bug fixes.
 * [2026-01] Introducing [R3L](https://github.com/shiweijiezero/R3L): a systematic reflect-then-retry RL mechanism with efficient language-guided exploration and stable off-policy learning ([paper](https://arxiv.org/abs/2601.03715)).
 * [2025-12] [[Release Notes]](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.4.0) Trinity-RFT v0.4.0 released: added [Tinker](https://thinkingmachines.ai/tinker/) backend for users **without GPUs**, add more benchmarks, enhance online RL and more.
 * [2025-12] Trinity-RFT powers the medical and health business of "Taobao Shangou", enabling the AI agent to understand vague symptoms, proactively ask follow-up questions, and provide precise recommendations ([News](https://tech.china.com.cn/sx/20251201/411376.shtml)).

README_zh.md

@@ -41,6 +41,7 @@ Trinity-RFT 面向不同背景和目标的用户提供相应功能：
 
 ## 🚀 新闻
 
+* [2026-01] [[发布说明]](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.4.1) Trinity-RFT v0.4.1 发布：升级 verl 至 v0.7.0，Tinker 后端支持 OpenAI API，修复若干 Bug。
 * [2026-01] 推出 [R3L](https://github.com/shiweijiezero/R3L)：基于反思-重试的强化学习机制，由自然语言反馈引导高效探索，并达成稳定的 off-policy 学习（[论文](https://arxiv.org/abs/2601.03715)）。
 * [2025-12] [[发布说明]](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.4.0) Trinity-RFT v0.4.0 发布：新增[Tinker](https://thinkingmachines.ai/tinker/) 后端以支持在 **无 GPU** 的设备上训练，增加更多基准测试，增强在线 RL 等功能。
 * [2025-12] Trinity-RFT 已支持 [tinker](https://thinkingmachines.ai/tinker/) 训练后端，可在**无 GPU 的设备**上进行模型训练。

docs/sphinx_doc/source/tutorial/trinity_configs.md

@@ -97,7 +97,7 @@ algorithm:
   repeat_times: 8
   optimizer:
     lr: 1e-6
-    warmup_style: "warmup"
+    lr_scheduler_type: "constant"
   # The following parameters are optional
   # If not specified, they will automatically be set based on the `algorithm_type`
   sample_strategy: "default"
@@ -111,7 +111,8 @@ algorithm:
 - `repeat_times`: Number of times each task is repeated. Default is `1`. In `dpo`, this is automatically set to `2`. Some algorithms such as GRPO and OPMD require `repeat_times` > 1.
 - `optimizer`: Optimizer configuration for actor.
   - `lr`: Learning rate for actor.
-  - `warmup_style`: Warmup style for actor's learning rate.
+  - `warmup_style`: Deprecated, use `lr_scheduler_type` instead. We will remove this field in future versions.
+  - `lr_scheduler_type`: Learning rate scheduler type for actor model. Default is `constant`. Supported types: `constant`, `consine`.
 - `sample_strategy`: The sampling strategy used for loading experiences from experience buffer. Supported types: `default`, `staleness_control`, `mix`.
 - `advantage_fn`: The advantage function used for computing advantages.
 - `kl_penalty_fn`: The KL penalty function used for computing KL penalty applied in reward.

docs/sphinx_doc/source_zh/tutorial/trinity_configs.md

@@ -97,7 +97,7 @@ algorithm:
   repeat_times: 8
   optimizer:
     lr: 1e-6
-    warmup_style: constant
+    lr_scheduler_type: constant
   # 以下参数为可选
   # 若未指定，将根据 `algorithm_type` 自动设置
   sample_strategy: "default"
@@ -111,7 +111,8 @@ algorithm:
 - `repeat_times`: 每个任务重复的次数。默认为 `1`。在 `dpo` 中自动设为 `2`。某些算法如 GRPO 和 OPMD 要求 `repeat_times` > 1。
 - `optimizer`: Actor 优化器的参数。
   - `lr`: 优化器的学习率。
-  - `warmup_style`: 学习率的预热策略。
+  - `warmup_style`：已弃用，请改用 `lr_scheduler_type`。该域将会在未来版本中移除。
+  - `lr_scheduler_type`：Actor 模型的学习率调度器类型。默认值为 `constant`。支持类型：`constant`、`cosine`。
 - `sample_strategy`: 从 experience buffer 加载 experience 时使用的采样策略。支持类型：`default`、`staleness_control`、`mix`。
 - `advantage_fn`: 用于计算优势值的函数。
 - `kl_penalty_fn`: 用于在奖励中计算 KL 惩罚的函数。

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name =  "trinity-rft"
-version = "0.4.0"
+version = "0.4.1"
 authors = [
     {name="Trinity-RFT Team", email="trinity-rft@outlook.com"},
 ]
@@ -88,7 +88,7 @@ tinker = [
 ]
 
 doc = [
-    "sphinx",
+    "sphinx<9.0.0",
     "sphinx-autobuild",
     "sphinx-book-theme",
     "myst-parser",

tests/common/vllm_test.py

@@ -1270,7 +1270,6 @@ def setUp(self):
         self.config.explorer.rollout_model.chat_template = CHAT_TEMPLATE
         self.config.explorer.rollout_model.enable_openai_api = True
         self.config.explorer.rollout_model.enable_lora = True
-        self.config.explorer.rollout_model.enable_runtime_lora_updating = True
 
         self.config.check_and_update()
         self.engines, self.auxiliary_engines = create_inference_models(self.config)
@@ -1345,3 +1344,68 @@ async def test_tinker_api(self):
         self.assertEqual(response.sequences[0].stop_reason, "length")
         self.assertEqual(len(prompt.to_ints()), len(response.prompt_logprobs))
         self.assertIsNone(response.topk_prompt_logprobs)
+
+        # test add remove lora
+        from vllm.lora.request import LoRARequest
+
+        # create a dummy lora adapter with all zero weights
+        lora_path_1 = os.path.join(self.config.checkpoint_job_dir, "adapter_1")
+        lora_path_2 = os.path.join(self.config.checkpoint_job_dir, "adapter_2")
+        _create_adapter(self.config.model.model_path, lora_path_1, "adapter_1")
+        _create_adapter(self.config.model.model_path, lora_path_2, "adapter_2")
+        lora_1 = LoRARequest(
+            lora_name="test_adapter_1",
+            lora_int_id=1,
+            lora_path=os.path.join(lora_path_1, "adapter_1"),
+        )
+        lora_2 = LoRARequest(
+            lora_name="test_adapter_2",
+            lora_int_id=2,
+            lora_path=os.path.join(lora_path_2, "adapter_2"),
+        )
+        response = await engine.sample.remote(
+            prompt=prompt,
+            num_samples=1,
+            sampling_params=types.SamplingParams(max_tokens=1),
+            include_prompt_logprobs=True,
+            lora_request=lora_1,
+        )
+        ids = await engine.list_lora_adapters.remote()
+        self.assertEqual(ids, [1])
+        self.assertEqual(len(response.sequences), 1)
+        self.assertEqual(response.sequences[0].stop_reason, "length")
+        self.assertEqual(len(prompt.to_ints()), len(response.prompt_logprobs))
+        self.assertIsNone(response.topk_prompt_logprobs)
+        response = await engine.sample.remote(
+            prompt=prompt,
+            num_samples=1,
+            sampling_params=types.SamplingParams(max_tokens=1),
+            include_prompt_logprobs=True,
+            lora_request=lora_2,
+        )
+        self.assertEqual(len(response.sequences), 1)
+        self.assertEqual(response.sequences[0].stop_reason, "length")
+        self.assertEqual(len(prompt.to_ints()), len(response.prompt_logprobs))
+        self.assertIsNone(response.topk_prompt_logprobs)
+        await engine.remove_lora_adapter.remote(lora_id=1)
+        await engine.remove_lora_adapter.remote(lora_id=2)
+        ids = await engine.list_lora_adapters.remote()
+        self.assertEqual(ids, [])
+
+
+def _create_adapter(model_path: str, lora_path: str, name: str):
+    from peft import LoraConfig, get_peft_model
+    from transformers import AutoModelForCausalLM
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        device_map="cpu",
+    )
+    lora_config = LoraConfig(
+        r=8,
+        lora_alpha=8,
+        target_modules=["gate_proj", "up_proj", "down_proj"],
+        lora_dropout=0.1,
+    )
+    lora_model = get_peft_model(model, lora_config, adapter_name=name)
+    lora_model.save_pretrained(lora_path)

trinity/__init__.py

@@ -1,4 +1,4 @@
 # -*- coding: utf-8 -*-
 """Trinity-RFT (Reinforcement Fine-Tuning)"""
 
-__version__ = "0.4.0"
+__version__ = "0.4.1"

trinity/common/models/vllm_model.py

@@ -403,6 +403,35 @@ async def logprobs(  # type: ignore [override]
             dtype=torch.float32,
         )
 
+    async def add_lora_adapter(self, lora_request: Any) -> int:
+        """Add a LoRA adapter to the vLLM engine.
+
+        Args:
+            lora_request (LoRARequest): The LoRA request.
+
+        Returns:
+            lora_id (int): The LoRA adapter ID.
+        """
+        lora_id = await self.async_llm.add_lora(lora_request)
+        return lora_id
+
+    async def remove_lora_adapter(self, lora_id: int) -> None:
+        """Remove a LoRA adapter from the vLLM engine.
+
+        Args:
+            lora_id (int): The LoRA adapter ID.
+        """
+        await self.async_llm.remove_lora(lora_id)
+
+    async def list_lora_adapters(self) -> Sequence[int]:
+        """List all LoRA adapter IDs in the vLLM engine.
+
+        Returns:
+            lora_ids (List[int]): The list of LoRA adapter IDs.
+        """
+        lora_ids = await self.async_llm.list_loras()
+        return list(lora_ids)
+
     async def sample(
         self,
         prompt: Any,