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What does this PR do?

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yyDing1 and others added 30 commits February 12, 2026 10:24
)

### What does this PR do?

as title

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…t#5279)

### What does this PR do?

gspo update config and add version with npu

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…e/train_mode reentrant (verl-project#5305)

### What does this PR do?

The `update_actor` call `train_mode` twice, the original
FSDPUlyssesShardingManager is not reentrant, result in global ulysess
group is still actor's sp group after update_actor.
```
ActorRolloutRefWorker.update_actor
               |
               v
TrainingWorker.train_mini_batch     <- train_mode (1)
               |
               v
TrainingWorker.train_batch          <- train_mode (2)
               |
               v
BaseEngine.train_batch
```
…ckend and remove temporarily unsupported TP/PP/CP parameters (verl-project#5303)

### What does this PR do?

as title.

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---------

Co-authored-by: Huazhong <[email protected]>
…#5302)

### What does this PR do?

Switch to the `MAX_UTILIZATION` scheduler for TensorRT LLM Rollout
Engine.

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### Test

Covered by existing TRTLLM tests.

### API and Usage Example

No API change.

### Design & Code Changes

`MAX_UTILIZATION` scheduler is beneficial to the rollout performance
without affecting the result. Long generation workload benefits most
from it.

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…oject#5301)

- Updated `AgentLoopWorker` to include a stable set of default extra
fields (`turn_scores`, `tool_rewards`, `is_cancel`,
`param_version_start`, `param_version_end`, `extras`) for consistent
downstream processing.
- Modified `SingleTurnAgentLoop` and `PartialSingleTurnAgentLoop` to
ensure the schema aligns with the updated agent loop structure by
initializing `turn_scores` and `tool_rewards` as empty lists in their
respective outputs.

### What does this PR do?

Currently each agent loop defines their own output / extra_info schema,
which makes it impossible to use a mixture of agent loops in combination
for training since they clash in batching. This PR makes agent-loop
outputs schema-stable by always emitting a consistent set of extra
fields, avoiding downstream conditional logic and preventing missing-key
failures when aggregating / post-processing rollouts.


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### Test

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### API and Usage Example

AgentLoopOutputs now always include these fields (even when
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```python
extra = {
  "turn_scores": [],          # default
  "tool_rewards": [],         # default
  "is_cancel": False,         # default
  "param_version_start": None,
  "param_version_end": None,
  "extras": {},               # default
}
```

### Design & Code Changes

* Ensure `AgentLoopWorker` initializes a stable default extra schema.
* Ensure `SingleTurnAgentLoop` and `PartialSingleTurnAgentLoop`
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consumers can rely on consistent types.

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---------

Co-authored-by: Denis Megerle <[email protected]>
Co-authored-by: Cursor <[email protected]>
…kends) (verl-project#5257)

### What does this PR do?
> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.

This PR mainly adds the following content for NPU (Ascend) platform:
- Add SAPO training script for Qwen3-8B model based on FSDP and vLLM
backends.

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### Test

Qwen3-8B SAPO training with dapo dataset:
<img width="1266" height="1514" alt="image"
src="https://github.com/user-attachments/assets/57ee2185-4b0f-4bd2-9239-573ec0290af9"
/>
<img width="2406" height="1384" alt="image"
src="https://github.com/user-attachments/assets/3345aa64-8669-4033-bc5d-10033e6eda7d"
/>


### API and Usage Example
> Demonstrate how the API changes if any, and provide usage example(s)
if possible.

```shell
# Run Qwen3-8B SAPO training (FSDP+ vLLM backend) on Ascend NPU
bash ./examples/sapo_trainer/run_qwen3_8b_sapo_npu.sh
```
### Design & Code Changes

> Demonstrate the high-level design if this PR is complex, and list the
specific changes.

Not related.

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---------

Co-authored-by: Xiaobo Hu <[email protected]>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
…P/VLLM backends) (verl-project#5250)

### What does this PR do?

Add NPU GRPO training scripts for Qwen3-VL-8B (FSDP/VLLM backends). The
reward curves of this scenario are also shown.

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### Test
<img width="1501" height="861" alt="20260209-144805"
src="https://github.com/user-attachments/assets/eb0f79cf-870a-4759-8ae3-ce586e2f5721"
/>


> For changes that can not be tested by CI (e.g., algorithm
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…DP/VLLM backends) (verl-project#5260)

### What does this PR do?

Add NPU GRPO training scripts for Qwen3-VL-30B (FSDP/VLLM backends). The
reward curves of this scenario are also shown.

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the test results in gpu and npu: 
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…t#5223)

### What does this PR do?

Fixes verl-project#5222


[PeftConfig](https://huggingface.co/docs/peft/en/package_reference/lora#peft.LoraConfig.target_modules)
from PEFT supports passing a list of strings as `target_modules`.

However, target_modules is annotated as `Optional[str]` in
`verl/workers/config/model.py` which leads to runtime errors when trying
to override its default `"all-linear"` value with e.g. a list encoded as
JSON string e.g. `["q_proj","v_proj"]`.

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### Design & Code Changes

I tried setting the type to `Optional[str | list[str]]` but was getting
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configs: omry/omegaconf#144

Instead I opted for `Any` and added type checks in `__post_init__`.

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Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
…e. (verl-project#4648)

### What does this PR do?

> Support LoRA training with FP16 using Megatron-Bridge.

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### What does this PR do?

Fix main pre-commit
verl-project#5319)

### What does this PR do?

as title.

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…ject#5320)

### What does this PR do?

This PR to make skip rollout compatible with async mode.

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…model (verl-project#5322)

### What does this PR do?

In VeOmni, the padding_scale for multimodal data differs from that for
text data. For details, refer to
https://github.com/ByteDance-Seed/VeOmni/blob/3c201b90f1a5726eef50e3cee6547b80770dac16/veomni/data/multimodal/data_collator.py#L70.

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---------

Co-authored-by: Huazhong <[email protected]>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
…verl-project#5190)

## Description
This PR adds support for NVFP4 Quantization-Aware Training (QAT) with
FSDP, enabling W4A16 (weight-only) quantization during RL training.

## What's included
verl/utils/qat/ module: QATLinear (Triton FP4 fake quantization), scale
fusion, NVFP4 quantizer, and vLLM dynamic weight loading patches
Recipe scripts and configs for Qwen3-30B-A3B W4A16 (full quantization &
FFN-only quantization)
Detailed README with implementation overview and experimental results

## Key Results
Validated on Qwen3-8B-Base (Dense) and Qwen3-30B-A3B-Base (MoE): W4A16
QAT achieves training accuracy on par with BF16 baseline, while without
QAT the KL divergence explodes and training crashes.
70.3% weight memory reduction on Qwen3-30B-A3B during rollout (56.88 GiB
→ 16.89 GiB), freeing ~40 GiB for additional KV Cache capacity.

verl-recipe PR:verl-project/verl-recipe#36 
README:
https://github.com/zhangyimi/verl-recipe/blob/dfbf09cd66c66ecbc4b9cea925a6885e5e53f2b1/qat/README.md

### What does this PR do?

> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.

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### Summary
This pull request adds information about the **MetaphorStar project**
(Awesome Work using Verl) to the README file.

### Changes
- Added a section in the README with links to the paper and GitHub
repository.
- Ensured that the links are functional and the information is
up-to-date.

### Related Issues
- Closes verl-project#5329
### What does this PR do?

The `workers` in `vLLMHttpServer` are redundant for assertions. This PR
removes it and replace with `num_workers`.

---------

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
…t#5333)

Reverts verl-project#5330

This PR introduces a breaking change that fails Fully-Async CIs. Should
be reverted.
…age data (verl-project#5343)

### What does this PR do?

Adds config files in `verl/experimental/` to package data in both
`pyproject.toml` and `setup.py`. This ensures that config files for
experimental entrypoints are included when verl is installed via pip
(non-editable mode).

This PR, together with verl-project#5209, is necessary to run the experimental
entrypoints when verl is installed in non-edit mode.

**Additional fix:** Also adds `trainer/config/*/*.yaml` pattern to
`setup.py` to match what's already in `pyproject.toml`, ensuring nested
trainer config files (in subdirectories like `actor/`, `critic/`, etc.)
are included. This fixes an existing inconsistency between the two
files.

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### Test

No tests needed - this only modifies packaging metadata. The correctness
can be verified by:
1. Installing verl via `pip install .` (non-editable)
2. Confirming that files exist: `python -c "import
verl.experimental.fully_async_policy.config;
print(verl.experimental.fully_async_policy.config.__file__)"`
3. Running an experimental entrypoint

### API and Usage Example

No API changes. This fix enables existing experimental entrypoints to
work when installed via pip.

### Design & Code Changes

**Changes:**
- `pyproject.toml`: Add `experimental/*/config/*.yaml` pattern
- `setup.py`: Add both `trainer/config/*/*.yaml` (fix existing bug) and
`experimental/*/config/*.yaml`

**Why both files?** Python packaging uses different files depending on
the build backend, so both need to be consistent.

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All checks passed ✓
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documentation](https://github.com/volcengine/verl/tree/main/docs) - Not
needed (internal packaging fix)
- [x] Add unit or end-to-end test(s) - This is packaging metadata that
only affects pip-installed packages, not editable installs used in CI.
- [ ] CI request
- [x] Recipe submodule - Not applicable
…roject#5345)

### What does this PR do?
torch-npu 2.7.1.post2 introduces support for configuring expandable
segments through the `torch.npu.memory._set_allocator_settings` method.

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…ool (verl-project#5348)

### What does this PR do?

fix verl-project#5342

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…roject#5051)

### What does this PR do?
Integrate Torchtitan as a new training engine in Verl. This PR
implements the basic APIs needed by Torchtitan Engine, and tested SFT
trainer in verl (qwen3 0.6b):
- Torchtitan Engine matches exactly with FSDP engine for SFT trainer
- `use_remove_padding=True` matches `use_remove_padding=False`
- TP/SP and FSDP work with both varlen and flex attention; numerics
match with single process.

**Relevant PRs:**
- Torchtitan side changes in
pytorch/torchtitan#2333.
- RFC for engine interfaces
verl-project#1371
- Training engine interface design
verl-project#1977
- Add Veomini Engine verl-project#4072

**Todos:**
See roadmap here: verl-project#5306
- [ ] enable parallelism: enable PP, EP, CP
- [ ] make Torchtitan Engine work with RL trainer 
- [ ] test multimodal input(ref:
https://github.com/verl-project/verl/pull/4492/changes)

### Checklist Before Starting

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### Test
```
MODEL_ID=Qwen/Qwen3-0.6B BACKEND=torchtitan bash tests/special_e2e/sft/run_sft_engine.sh
MODEL_ID=Qwen/Qwen3-0.6B BACKEND=fsdp bash tests/special_e2e/sft/run_sft_engine.sh
```
`use_remove_padding=True`
<img width="1372" height="658" alt="image"
src="https://github.com/user-attachments/assets/42c01ce6-f561-4c81-a562-e412a51ac296"
/>
`use_remove_padding=False`
<img width="1353" height="610" alt="image"
src="https://github.com/user-attachments/assets/c8db130e-626c-4d38-8932-9b3218431da3"
/>

Test TP and FSDP
<img width="1324" height="683" alt="image"
src="https://github.com/user-attachments/assets/12f8414d-041c-41fb-b915-012ed75c4adb"
/>



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…CE logsumexp (verl-project#5349)

### What does this PR do?

Fixes verl-project#2656 and verl-project#2899. When `vocab_size % BLOCK_SIZE_N != 0`, the final
Triton tile loads out-of-bounds weight rows (padded to zero by
`tl.load`), contributing `exp(0) = 1` per phantom token to the logsumexp
accumulator. This inflates the denominator and corrupts
log-probabilities and entropy. For uniform distributions this remains
undetected, but impacts severely (up to 100% token mismatch) when the
softmax is peaked, as commonly seen during RL training.

Fix: mask OOB positions to `-inf` before the running max/exp
accumulation via `tl.where`. The original (zero-padded) logits are kept
for the entropy accumulator.

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lce vocab
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  ### Test

Added `test_lce_non_divisible_vocab_padding` to
`tests/utils/test_linear_cross_entropy.py`. The test constructs a peaked
weight matrix (`w[:, 0] = -15·T`, `w[0, 0] = 3·T`, `vocab_size=152064`
which has mod 1024 =
512) to reliably trigger the bug, plus a divisible-vocab control case.

  GPU results (H200, `vocab_size=152064`):

  | Case | Without fix | With fix |
  |---|---|---|
| non-divisible vocab (mod1024=512) | max\_diff=3.274581,
mismatch=100.0% ❌ | max\_diff=0.000001, mismatch=0.0% ✅ |
| divisible vocab (mod1024=0) | max\_diff=0.000000, mismatch=0.0% ✅ |
max\_diff=0.000000, mismatch=0.0% ✅ |

  ### API and Usage Example

  No API changes.

  ### Design & Code Changes

- `verl/utils/kernel/kernels.py`
(`efficient_entropy_kernel_general_mainloop`): compute `vocab_bound =
min((pid_n + 1) * vocab_per_split, vocab_size)`, derive `logits_for_lse
= tl.where(offs_bn[None, :] <
vocab_bound, logits, float("-inf"))`, use it for the running max and
exp-sum while keeping original `logits` for the entropy accumulator.
- `tests/utils/test_linear_cross_entropy.py`: add
`test_lce_non_divisible_vocab_padding`.

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…o support R3 router replay (verl-project#5368)

Include routed_experts in ToolAgentLoop return value to support R3
router replay

### What does this PR do?

Previously, the return value of the `ToolAgentLoop` class omitted the
`agent_data.routed_experts` field, which caused errors during R3 router
replay training. This PR includes `routed_experts` in the
`ToolAgentLoop` return value to resolve this issue.

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### What does this PR do?

Pass torch dtype when init random model, otherwise, the dummy model will
be created in fp32 even if the original model weight is in bf16, which
causes storage space waste.

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Signed-off-by: Hollow Man <[email protected]>
### What does this PR do?

cupy-cuda12x==14.0.0 requires numpy>=2.0.0, which conflicts with pandas
and some other packages.
…rsion info (verl-project#5324)

### What does this PR do?

ascend add performance analysis guide and update some version info

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pengwu22 and others added 30 commits February 25, 2026 00:00
…ts (verl-project#5310)

### What does this PR do?

Fix CI failures caused by occasional port conflicts

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…t vLLM OOM (verl-project#5384)

### What does this PR do?

Adds `aggressive_empty_cache(force_sync=True)` at the end of
`ActorRolloutRefWorker.init_model()` to prevent vLLM from OOMing at
startup when colocated on the same GPUs as FSDP.

Related: verl-project#4229, verl-project#4257 (stale)

After the removal of `ExternalZeroMQDistributedExecutor`, vLLM runs in
separate MP worker processes instead of inside the FSDP worker process.
During `init_model()`, PyTorch's CUDA allocator reserves large transient
blocks for full-model loading before FSDP sharding and
`sync_module_states` broadcasting. After init, these blocks are no
longer needed but remain cached by the allocator (`cudaMalloc`'d, not
`cudaFree`'d). Since vLLM now runs in a separate process with its own
allocator, it cannot reuse these cached blocks — `cudaMemGetInfo`
reports them as "used", and vLLM fails its `gpu_memory_utilization`
check.

Previous attempts to fix this (verl-project#4257) went stale. This approach is
simpler: one line, no guards needed, and is a no-op when there is
nothing to free.

### Checklist Before Starting

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### Test

This cannot be tested in CI because the OOM is a cross-process CUDA
memory visibility issue that requires colocated FSDP + vLLM on the same
physical GPU to reproduce.

- The call site is exercised by the existing
`tests/workers/test_fsdp_workers.py`
- Validated experimentally with a colocated FSDP + vLLM training run (8B
VLM, 8x H200, hybrid mode)
- The fix is a no-op when there is no cached memory to free, so it is
safe in all configurations

### API and Usage Example

No API changes. The fix is automatic.

### Design & Code Changes

One line added at the end of `ActorRolloutRefWorker.init_model()` in
`verl/workers/fsdp_workers.py`:

```python
# Free cached GPU memory so colocated vLLM processes can see it via cudaMemGetInfo
aggressive_empty_cache(force_sync=True)
```

This pattern already exists in the codebase:
- `megatron_workers.py:677` — `empty_cache()` at end of `init_model`
- `fsdp_workers.py:742` — `aggressive_empty_cache` during
`rollout_mode()` context switch
- `engine_workers.py:671` — `aggressive_empty_cache` during rollout mode

The FSDP worker was the only one missing it at init time.

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### What does this PR do?

There's nothing specific in Megatron-Bridge that stops MTP support. 

NVIDIA-NeMo/Megatron-Bridge#2387 adds MiMo dense
MTP models bridge support so that
`examples/mtp_trainer/test_dapo_mimo_7b_with_mtp_math_megatron.sh` can
also be used together with Megatron-Bridge (setting `vanilla_mbridge` to
be `False`).

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---------

Signed-off-by: Hollow Man <[email protected]>
### What does this PR do?

Based on ckpt engine abstraction [add checkpoint-engine
abstraction](verl-project#4775), in this PR,
we add kimi_ckpt_engine backend to support both GPU and huawei Ascend
NPU.

Since establishing communication domains across trainer and rollout
workers is required, this PR also depends on the [newly added
communication domain
support](MoonshotAI/checkpoint-engine#66) in
kimi_ckpt_engine.

TODO:

- [x] Add detailed performance testing results in checkpoint engine
README.

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We have verified the functionality on both GPU and NPU. Performance
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---------

Co-authored-by: kip-cxj <[email protected]>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
…rightconfig to fix breaks (verl-project#5385)

### What does this PR do?

This PR ignores the `pyrightconfig.json` at repo root to allow users to
customize the pyright config, allowing to fix problems like that `vllm`
can not be found.

c.f. https://docs.basedpyright.com/latest/configuration/config-files/

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---------

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
### What does this PR do?

update triton-ascend == 3.2.0
fix failed npu ut

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Co-authored-by: wucong25 <[email protected]>
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… to support multiple checkpoint engine backends (verl-project#5029)

### What does this PR do?

> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.

Refactor fully-async and one-step-off training to support multiple
checkpoint engine backends

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Tested on 1*A2 with gsm8k and qwen3-0.6B with expandable memory:



![70508cf1-2a64-4d04-84a4-b26bcbf1f778](https://github.com/user-attachments/assets/c203901b-afe6-4283-92f0-5f2865238a2a)

![537d4dd2-2bf4-4020-9d51-8f12242f8aae](https://github.com/user-attachments/assets/55cb3576-196b-4989-861e-200bfa499291)

![02879e43-c1e3-439c-8cd9-5db1f3c1aff6](https://github.com/user-attachments/assets/a003e62f-db45-4dc2-9cd6-eb930a34feaa)

Also tested with rollout_corr TIS:
Fully-async:
<img width="2044" height="426" alt="image"
src="https://github.com/user-attachments/assets/05416e90-48aa-475a-85e1-b53095948b2f"
/>
One-step-off:
<img width="1995" height="399" alt="image"
src="https://github.com/user-attachments/assets/3963be27-a36d-4f89-ac45-e0b89e1455e2"
/>



### API and Usage Example

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if possible.
<img width="3376" height="846" alt="graphviz (4)"
src="https://github.com/user-attachments/assets/2a9606e5-831b-402d-a511-bf6a1d804c19"
/>




```python
# Add code snippet or script demonstrating how to use this
```

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…l-project#5404)

### What does this PR do?

add `fully async` and `one step off` to PR Checklist.

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---------

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
…project#5408)

### What does this PR do?

Updated the weight and dataset download methods, and added the script
for starting multiple devices.

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…l-project#5397)

### What does this PR do?

This PR adds the official implementation of
[DPPO](https://arxiv.org/pdf/2602.04879). It replaces the heuristic
ratio clipping with a more principled constraint based on a direct
estimation of policy divergence (e.g. Total Variation or KL).

Reference Paper: [Rethinking the Trust Region in LLM Reinforcement
Learning](https://arxiv.org/pdf/2602.04879)
Original Repo: [Stable-RL](https://github.com/sail-sg/Stable-RL)

The results of running Qwen3-30B-A3B-Base on DAPO dataset:

<img width="1338" height="606" alt="image"
src="https://github.com/user-attachments/assets/12e88623-8408-4a43-84ce-6ba95c203f8b"
/>

<img width="1352" height="624" alt="image"
src="https://github.com/user-attachments/assets/5230c3ce-2a24-4c83-a3fc-77f7d33125f5"
/>

<img width="1360" height="620" alt="image"
src="https://github.com/user-attachments/assets/8b8e978f-e802-4952-85ed-37dabdacf957"
/>


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### What does this PR do?

fix npu best practice doc

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…s in seq-mean-token-sum-norm (verl-project#5417)

### What does this PR do?

This PR:

1. fixes the "seq-mean" semantics in "seq-mean-token-sum-norm", which is
missing in the previous implementation; (Note that here this is
implemented by merging the "seq-mean-token-sum" part of
"seq-mean-token-sum-norm" into the "seq-mean-token-sum" branch.)
2. reverts to the design that defaults `loss_scale_factor` to the
horizon `loss_mask.shape[-1]`, which is actually a reasonable design;
3. reverts the `loss_agg_mode` to "seq-mean-token-sum-norm" in PR verl-project#5397 

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…project#5395)

### What does this PR do?

> When using PyTorch Profiler for multi-step performance analysis, the
performance analysis files generated by different steps will overwrite
each other in discrete mode, resulting in only the analysis data from
the last step being retained. This PR fix this bug by adding timestamp
in the file_name. Issue is:
verl-project#5387.

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…_padding function (verl-project#5410)

### What does this PR do?

Fix the context parallel to align the tensor size

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…5420)

### What does this PR do?

> Fixes a compatibility issue in `ToolAgentLoop`, downstream
image-processing pipelines require passing passing `images=None`
(instead of an empty list) when a tool call returns no new images.

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### Design & Code Changes

> - In `verl/experimental/agent_loop/tool_agent_loop.py`, normalize
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Co-authored-by: Denis Megerle <[email protected]>
… async rollouter (verl-project#5359)

### What does this PR do?

> Make `FullyAsyncRollouter` honor existing `data.train_max_samples` /
`data.val_max_samples` config by passing them through to
`create_rl_dataset(max_samples=...)` (matching the behavior of the
non-fully-async trainer paths).

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### Design & Code Changes

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- `verl/experimental/fully_async_policy/fully_async_rollouter.py`: pass
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Co-authored-by: Denis Megerle <[email protected]>
…verl-project#5425)

### What does this PR do?

> Remove unused tool schema initialization in `SingleTurnAgentLoop` and
stop passing tool schemas into `apply_chat_template()`.
>
> **Note:** After this change, `SingleTurnAgentLoop` no longer includes
tool/function schemas in the prompt construction path. This matches the
current single-turn flow (no tool execution in `run()`), and avoids
unnecessary tool config reads + schema dumping.

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Made with [Cursor](https://cursor.com)

Co-authored-by: Denis Megerle <[email protected]>
… from rollout (verl-project#5418)

### What does this PR do?


verl-project#5400 (comment),
get rid of actor_rollout_ref from rollout:
- AgentLoopManager
- AgentLoopWorker
- AgentLoopBase: SingleTurnAgentLoop, ToolAgentLoop

Eventually, we should get rid of actor_rollout_ref in
`verl/trainer/config/ppo_trainer.yaml` with flattend actor, ref, rollout
fields:
```python
actor:
  _target_: verl.workers.config.FSDPActorConfig
  ...

ref:
  _target_: verl.workers.config.FSDPActorConfig
 ...

rollout:
  _target_: verl.workers.config.RolloutConfig
  ...
```
…dockerfile (verl-project#5431)

### What does this PR do?
as title.

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### What does this PR do?

fix npu docs

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---------

Co-authored-by: yaochao20 <[email protected]>
…l-project#5454)

### What does this PR do?

In verl 0.8, we will refactor the integration approach for
TransferQueue. For reference:
verl-project#5401.

This PR will first clean up the legacy code to facilitate the subsequent
TQ integration.

CC @zw0610 @wuxibin89 

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Signed-off-by: 0oshowero0 <[email protected]>
…#5446)

### What does this PR do?

Fix failed trtllm_unit_tests, Logs at
https://github.com/verl-project/verl/actions/runs/22511591314/job/65221753054

```logs
        if not self._ray_is_cross_language:
>           raise AttributeError(
                f"'{type(self).__name__}' object has " f"no attribute '{item}'"
            )
E           AttributeError: 'ActorHandle' object has no attribute '_get_attribute'

/usr/local/lib/python3.12/dist-packages/ray/actor.py:2238: AttributeError
```

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Signed-off-by: Hollow Man <[email protected]>
…P timeout (verl-project#5451)

### What does this PR do?
After f5c34bb added a `dp_group is not None` guard in in
verl/utils/seqlen_balancing.py

https://github.com/verl-project/verl/blob/c179476754150a5384f96d56b622a8f6330d2c04/verl/utils/seqlen_balancing.py#L391,
the megatron actor/critic calls to rearrange_micro_batches (which do not
pass dp_group) silently skipped the num_micro_batches all_reduce across
data-parallel ranks.

Under dynamic batching, this caused different DP ranks to compute
different numbers of micro-batches, leading to a deadlock in MoE DeepEP
all-to-all dispatch and eventually triggering: DeepEP error: CPU recv
timeout

Fix:
Pass `mpu.get_data_parallel_group()` as dp_group in both
megatron_actor.py and megatron_critic.py to restore proper DP
synchronization.

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Signed-off-by: xhx1022 <[email protected]>
…ect#5402)

### What does this PR do?

Remove the unexpected concurrency bound of 1000 that preventing rollout
engine from reaching `actor_rollout_ref.rollout.max_num_seqs` if it's
larger than 1000.

[Ray
doc](https://docs.ray.io/en/latest/ray-core/api/doc/ray.actor.ActorClass.options.html#ray.actor.ActorClass.options:~:text=calls%20is%20unlimited.-,max_concurrency,-%E2%80%93%20The%20max%20number)
says:

```
            max_concurrency: The max number of concurrent calls to allow for
                this actor. This only works with direct actor calls. The max
                concurrency defaults to 1 for threaded execution, and 1000 for
                asyncio execution. Note that the execution order is not
                guaranteed when max_concurrency > 1.
```

and the call to `{TRTLLM,vLLM,SGLang}HttpServer.generate` is an async
remote call:


https://github.com/verl-project/verl/blob/6f4942b1153b23720e74564e00817526b342198c/verl/experimental/agent_loop/agent_loop.py#L114-L120

So the default value will limit the request concurrency to 1000.

This PR sets `max_concurrency` based on
`actor_rollout_ref.rollout.max_num_seqs` so that a higher concurrency
configured by the user can be achieved.

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### Test

Current tests should be enough to ensure it does not break anything.

### Checklist Before Submitting

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