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Author SHA1 Message Date
ℍ𝕠𝕝𝕝𝕠𝕨 𝕄𝕒𝕟
acfcf98ed0 [doc] fix: actor_rollout_ref.critic is not correct (#3778) 
### 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.

They should start directly with `critic`

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Signed-off-by: Hollow Man <hollowman@opensuse.org>
 2025-10-16 11:12:45 +08:00
EduardDurech
26a734e740 [algo, perf] feat: Vectorize RLOO Advantage Estimator - 20x Speedup (#3555) 
Vectorize RLOO advantage estimator
130ms -> 6ms
Similar method can be done for other advantage estimators, I just don't
have time

Implements

$$r_i - \frac{\sum_{j\ne i} r_j}{G-1} = \frac{(G-1)r_i - \sum_{j\ne i}
r_j}{G-1} = \frac{G r_i - \sum_{j\in g} r_j}{G-1}$$

<img width="2199" height="628" alt="image"
src="https://github.com/user-attachments/assets/339e5bd2-6949-4460-a297-34268ffc1764"
/>
 2025-09-24 17:36:41 +08:00
Feng Yao
b8dc5377c6 [BREAKING][vllm, fsdp] feat: add Rollout-Training Mismatch Fix -- Truncated importance sampling (#2953) 
### What does this PR do?

Support [vLLM-FSDP off-policy importance sampling
correction](https://fengyao.notion.site/off-policy-rl) using Truncated
Importance Sampling (TIS):

<img width="859" height="382" alt="TIS"
src="https://github.com/user-attachments/assets/adc8f797-aa14-4b29-b265-a682c281d08e"
/>




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### API and Usage Example

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```python
python3 -m verl.trainer.main_ppo \
    algorithm.adv_estimator=gae \
    data.train_files="$train_files" \
    data.val_files="$test_files" \
    data.train_batch_size=1024 \
    data.max_prompt_length=1024 \
    data.max_response_length=1024 \
    data.filter_overlong_prompts=True \
    data.truncation='error' \
    actor_rollout_ref.model.path=Qwen/Qwen2.5-32B-Instruct \
    actor_rollout_ref.model.enable_gradient_checkpointing=False \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.ppo_mini_batch_size=256 \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \
    actor_rollout_ref.model.enable_gradient_checkpointing=True \
    actor_rollout_ref.actor.fsdp_config.param_offload=False \
    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
    actor_rollout_ref.actor.use_kl_loss=False \
    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=16 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=4 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.5 \
    critic.optim.lr=1e-5 \
    critic.model.use_remove_padding=True \
    critic.model.path=Qwen/Qwen2.5-32B-Instruct \
    critic.model.enable_gradient_checkpointing=False \
    critic.ppo_micro_batch_size_per_gpu=8 \
    critic.model.fsdp_config.param_offload=False \
    critic.model.fsdp_config.optimizer_offload=False \
    algorithm.use_kl_in_reward=False \
    trainer.critic_warmup=0 \
    trainer.logger='["console","wandb"]' \
    trainer.project_name='verl_example' \
    trainer.experiment_name='Qwen2.5-32B-Instruct_function_rm' \
    trainer.n_gpus_per_node=8 \
    trainer.nnodes=4 \
    trainer.save_freq=20 \
    trainer.test_freq=10 \
    trainer.total_epochs=15 \
    actor_rollout_ref.rollout.calculate_log_probs=True \   # add this config to return rollout prob
    +actor_rollout_ref.actor.behav_imp_weight_cap=10.0$@   # add this config to set up C value in TIS
```

### Design & Code Changes

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

Co-authored-by: Narsil-Dinghuai Zhang 张鼎怀 <dinghuai233@gmail.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: LiyuanLucasLiu <llychinalz@gmail.com>
 2025-08-26 14:06:07 -07:00
Kai-Hsun Chen
a31a8f251f [doc] fix: quickstart example can't work on zsh (#2509) 
### What does this PR do?

I followed the instructions at
https://verl.readthedocs.io/en/latest/start/quickstart.html to run the
PPO example on my devbox, which uses zsh. However, I got the error zsh:
no matches found: `trainer.logger=[console]` because `[]` is interpreted
as a glob pattern in zsh.

```
(verl) ➜  verl git:(20250713-devbox-2-tmux0-verl-2) ✗ PYTHONUNBUFFERED=1 python3 -m verl.trainer.main_ppo \
 data.train_files=$HOME/data/gsm8k/train.parquet \
 data.val_files=$HOME/data/gsm8k/test.parquet \
 data.train_batch_size=256 \
 data.max_prompt_length=512 \
 data.max_response_length=256 \
 actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \
 actor_rollout_ref.actor.optim.lr=1e-6 \
 actor_rollout_ref.actor.ppo_mini_batch_size=64 \
 actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \
 actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \
 actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
 actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \
 actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \
 critic.optim.lr=1e-5 \
 critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \
 critic.ppo_micro_batch_size_per_gpu=4 \
 algorithm.kl_ctrl.kl_coef=0.001 \
 trainer.logger=['console'] \
 trainer.val_before_train=False \
 trainer.n_gpus_per_node=1 \
 trainer.nnodes=1 \
 trainer.save_freq=10 \
 trainer.test_freq=10 \
 trainer.total_epochs=15 2>&1 | tee verl_demo.log
zsh: no matches found: trainer.logger=[console]
```

This PR has 3 changes:
* `trainer.logger=['console']` -> `trainer.logger=console`
* `trainer.logger=['console','wandb']` ->
`trainer.logger='["console","wandb"]'`
* `trainer.logger=['console','tensorboard']` ->
`trainer.logger='["console","tensorboard"]'`

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### Test

* `trainer.logger=console` (zsh)
<img width="898" height="564" alt="image"
src="https://github.com/user-attachments/assets/a957a493-75e6-462b-9974-6b1c4cdf5a80"
/>

* ``trainer.logger='["console","wandb"]'`` (zsh)
<img width="870" height="565" alt="image"
src="https://github.com/user-attachments/assets/e20613bf-2ccc-4653-b23f-90edc3d568d1"
/>

* `trainer.logger=console` (bash)
  ```bash
ubuntu@ip-xxx-xx-x-xxx:~/verl$ PYTHONUNBUFFERED=1 python3 -m
verl.trainer.main_ppo \
  >  data.train_files=$HOME/data/gsm8k/train.parquet \
  >  data.val_files=$HOME/data/gsm8k/test.parquet \
  >  data.train_batch_size=256 \
  >  data.max_prompt_length=512 \
  >  data.max_response_length=256 \
  >  actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \
  >  actor_rollout_ref.actor.optim.lr=1e-6 \
  >  actor_rollout_ref.actor.ppo_mini_batch_size=64 \
  >  actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \
  >  actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \
  >  actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
  >  actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \
  >  actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \
  >  critic.optim.lr=1e-5 \
  >  critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \
  >  critic.ppo_micro_batch_size_per_gpu=4 \
  >  algorithm.kl_ctrl.kl_coef=0.001 \
  >  trainer.logger=console \
  >  trainer.val_before_train=False \
  >  trainer.n_gpus_per_node=1 \
  >  trainer.nnodes=1 \
  >  trainer.save_freq=10 \
  >  trainer.test_freq=10 \
  >  trainer.total_epochs=15 2>&1 | tee verl_demo.log
2025-07-14 02:52:27,669 INFO worker.py:1908 -- Started a local Ray
instance. View the dashboard at 127.0.0.1:8265
(TaskRunner pid=1799248) TaskRunner hostname: ip-172-31-9-244, PID:
1799248
(TaskRunner pid=1799248) {'actor_rollout_ref': {'actor': {'checkpoint':
{'load_contents': ['model',
(TaskRunner pid=1799248) 'optimizer',
(TaskRunner pid=1799248) 'extra'],
(TaskRunner pid=1799248) 'save_contents': ['model',
(TaskRunner pid=1799248) 'optimizer',
(TaskRunner pid=1799248) 'extra']},
  ```

* `trainer.logger='["console","wandb"]'` (bash)
  ```bash
ubuntu@ip-xxx-xx-x-xxx:~/verl$ PYTHONUNBUFFERED=1 python3 -m
verl.trainer.main_ppo \
  >  data.train_files=$HOME/data/gsm8k/train.parquet \
  >  data.val_files=$HOME/data/gsm8k/test.parquet \
  >  data.train_batch_size=256 \
  >  data.max_prompt_length=512 \
  >  data.max_response_length=256 \
  >  actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \
  >  actor_rollout_ref.actor.optim.lr=1e-6 \
  >  actor_rollout_ref.actor.ppo_mini_batch_size=64 \
  >  actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \
  >  actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \
  >  actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
  >  actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \
  >  actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \
  >  critic.optim.lr=1e-5 \
  >  critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \
  >  critic.ppo_micro_batch_size_per_gpu=4 \
  >  algorithm.kl_ctrl.kl_coef=0.001 \
  >  trainer.logger='["console","wandb"]' \
  >  trainer.val_before_train=False \
  >  trainer.n_gpus_per_node=1 \
  >  trainer.nnodes=1 \
  >  trainer.save_freq=10 \
  >  trainer.test_freq=10 \
  >  trainer.total_epochs=15 2>&1 | tee verl_demo.log
2025-07-14 02:54:13,989 INFO worker.py:1908 -- Started a local Ray
instance. View the dashboard at 127.0.0.1:8265
(TaskRunner pid=1805000) TaskRunner hostname: ip-172-31-9-244, PID:
1805000
(TaskRunner pid=1805000) {'actor_rollout_ref': {'actor': {'checkpoint':
{'load_contents': ['model',
(TaskRunner pid=1805000) 'optimizer',
(TaskRunner pid=1805000) 'extra'],
(TaskRunner pid=1805000) 'save_contents': ['model',
(TaskRunner pid=1805000) 'optimizer',
(TaskRunner pid=1805000) 'extra']},
  ```

### API and Usage Example

No

### Design & Code Changes

No

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---------

Signed-off-by: Kai-Hsun Chen <kaihsun@anyscale.com>
 2025-07-14 13:26:32 +08:00
shuyhere
281ecd4cc1 [doc] fix: Fix document config.rst (#2369) 
### What does this PR do?

> Fix document config.rst: the parameter“gemma” -> “gamma”.

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 2025-07-05 09:26:42 -07:00
xichengpro
ccefcf05ca [doc] fix: Fix mismatched config description for ppo_epochs in critic (#2102) 
### Checklist Before Starting

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### What does this PR do?

> Fix mismatched config description for `ppo_epochs` in critic

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 2025-06-19 18:19:31 +08:00
H
cef6361def [docs] lora: fix lora image and add GRPO docs (#1788) 
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### What does this PR do?

Fix image rendering
 2025-06-01 09:49:42 +08:00