[skyrl-train] Refactor TIS to use more comprehensive off policy correction config

skyrl-train/docs/algorithms/custom_algorithms.rst

@@ -48,14 +48,14 @@ Similarly, you can register custom policy loss functions:
 
 .. code-block:: python
 
-   from skyrl_train.utils.ppo_utils import register_policy_loss, PolicyLossRegistry
+   from skyrl_train.utils.ppo_utils import register_policy_loss, PolicyLossRegistry, LossMetrics
 
    @register_policy_loss("reinforce")
    def compute_reinforce_policy_loss(log_probs, old_log_probs, advantages, config, loss_mask=None, rollout_log_probs=None):
        # Your custom policy loss implementation (like REINFORCE)
        loss = (-log_probs * advantages).mean()
-       # return loss and clip ratio
-       return loss, 0.0
+       # return loss and loss metrics
+       return loss, LossMetrics(clip_ratio=0.0)
 
 Registry Ray Distribution
 ~~~~~~~~~~~~~~~~~~~~~~~~~~

skyrl-train/docs/configuration/config.rst

@@ -389,6 +389,45 @@ Algorithm Configuration
       # dual clip parameters
       clip_ratio_c: 3.0
 
+      # To be deprecated in favor of off_policy_correction.tis_ratio_type = "token"
+      # and "token_tis_ratio_clip_high"
+      use_tis: false 
+      tis_imp_ratio_cap: -1.0
+
+      # references
+      # - https://github.com/szrlee/verl/blob/yingru/rollout_correction/docs/advance/rollout_corr_math.md
+      # - https://fengyao.notion.site/off-policy-rl
+      off_policy_correction:
+        # type of importance sampling ratio to use for ppo loss correction
+        # here importance sampling ratio refers to exp(logprobs_{policy_old} - logprobs_{rollout_policy})
+        tis_ratio_type: null # null, "token", "sequence"
+
+        # used if tis_ratio_type = "token", 1.5-5.0 is recommended for "token" tis_ratio_type
+        token_tis_ratio_clip_high: 2.0
+        # used if tis_ratio_type = "sequence", 2.0-10.0 is recommended for "sequence" tis_ratio_type
+        sequence_tis_ratio_clip_high: 5.0
+
+        # method of masking out sequences with cumulative importance sampling ratios outside the cap
+        # "product" masks out sequences with product of importance ratios outside the cap
+        # "geometric" masks out sequences with geometric mean of importance ratios outside the cap
+        sequence_mask_metric: null # null, "product", "geometric"
+
+        # used if sequence_mask_metric = "geometric"
+        # values around 0.99-1.01 are recommended for "geometric" sequence_mask_metric - MoE models may need larger allowed ranges due to higher mismatch
+        geo_mask_high: 1.01
+        geo_mask_low: 0.99
+
+        # used if sequence_mask_metric = "product"
+        # values around 0.5-2.0 are recommended for "product" sequence_mask_metric
+        product_mask_high: 2.0
+        product_mask_low: 0.5
+
+        # separate from sequence_mask_metric and tis_ratio_type 
+        # if any off_policy_correction is enabled, masks out sequences with any token having importance ratio
+        # far outside an acceptable range (low and high thresholds)
+        outlier_token_is_threshold_low: 1e-4
+        outlier_token_is_threshold_high: 100
+
       # clip-cov parameters (only used when policy_loss_type: "clip_cov")
       clip_cov:
         clip_ratio: 0.0002 # fraction of tokens to clip based on covariance
@@ -413,10 +452,6 @@ Algorithm Configuration
         type: null # filter (DAPO), replace (POLARIS/WebSailor), or null
         max_sample_batches: 30 # sample at most this many batches before stopping, -1 to sample forever
         min_replace_ratio: 0.3 # minimum proportion of good samples with which to replace bad samples (for replace strategy only)
-
-      # Truncated Importance Sampling as proposed in https://fengyao.notion.site/off-policy-rl 
-      use_tis: false 
-      tis_imp_ratio_cap: -1.0
 
       # SAPO parameters (only used when policy_loss_type: "sapo") (https://arxiv.org/pdf/2511.20347)
       sapo:
@@ -466,8 +501,8 @@ Algorithm Configuration
   - ``algorithm.dynamic_sampling.type``: Type of dynamic sampling to use. Currently, we support ``filter`` (`DAPO <https://dapo-sia.github.io/>`_), ``replace`` (`POLARIS <https://hkunlp.github.io/blog/2025/Polaris/>`_ / `WebSailor <https://arxiv.org/abs/2507.02592>`_), or ``null`` for no dynamic sampling.
   - ``algorithm.dynamic_sampling.max_sample_batches``: Maximum number of batches to sample before stopping. Set to ``-1`` to sample forever.
   - ``algorithm.dynamic_sampling.min_replace_ratio``: Minimum proportion of good samples with which to replace bad samples for ``replace`` strategy.
-- ``algorithm.use_tis``: Whether to use Truncated Importance Sampling (TIS) as proposed in `this blog <https://fengyao.notion.site/off-policy-rl>`_. 
-- ``algorithm.tis_imp_ratio_cap``: Cap parameter for the importance ratio in TIS.
+- ``algorithm.use_tis``: Whether to use Truncated Importance Sampling (TIS) as proposed in `this blog <https://fengyao.notion.site/off-policy-rl>`_. This flag is to be deprecated, use ``off_policy_correction.tis_ratio_type = "token"`` instead.
+- ``algorithm.tis_imp_ratio_cap``: Cap parameter for the importance ratio in TIS. This flag is to be deprecated, use ``off_policy_correction.token_tis_ratio_clip_high`` instead.
 - ``algorithm.clip_cov``: Clip-Cov parameters (only used when ``policy_loss_type`` is ``clip_cov``):
 
   - ``clip_ratio``: Fraction of tokens to clip based on covariance values.
@@ -489,6 +524,35 @@ Algorithm Configuration
   - ``tau_pos``: Temperature for gating function for tokens with positive advantages.
   - ``tau_neg``: Temperature for gating function for tokens with negative (or zero) advantages.
 
+Off Policy Correction Configuration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+- ``algorithm.off_policy_correction``: Off policy correction configuration. See the full configuration below
+
+.. code-block:: yaml
+
+  off_policy_correction:
+    tis_ratio_type: null # null, "token", "sequence"
+    token_tis_ratio_clip_high: 2.0
+    sequence_tis_ratio_clip_high: 5.0
+    sequence_mask_metric: null # null, "product", "geometric"
+    geo_mask_high: 1.01
+    geo_mask_low: 0.99
+    product_mask_high: 2.0
+    product_mask_low: 0.5
+    outlier_token_is_threshold_low: 1e-4
+    outlier_token_is_threshold_high: 100
+
+- ``algorithm.off_policy_correction.tis_ratio_type``: Type of importance sampling ratio to use for ppo loss correction. Options include: ``null``, ``token``, ``sequence``.
+- ``algorithm.off_policy_correction.token_tis_ratio_clip_high``: Cap parameter for "token" tis_ratio_type.
+- ``algorithm.off_policy_correction.sequence_tis_ratio_clip_high``: Cap parameter for "sequence" tis_ratio_type.
+- ``algorithm.off_policy_correction.sequence_mask_metric``: Method of masking out sequences with cumulative importance sampling ratios outside the cap. Options include: ``null``, ``product``, ``geometric``.
+- ``algorithm.off_policy_correction.geo_mask_high``: High threshold for "geometric" sequence_mask_metric.
+- ``algorithm.off_policy_correction.geo_mask_low``: Low threshold for "geometric" sequence_mask_metric.
+- ``algorithm.off_policy_correction.product_mask_high``: High threshold for "product" sequence_mask_metric.
+- ``algorithm.off_policy_correction.product_mask_low``: Low threshold for "product" sequence_mask_metric.
+- ``algorithm.off_policy_correction.outlier_token_is_threshold_low``: Low threshold for outlier token mask - masks out sequences with any token having importance ratio far outside an acceptable range (low and high thresholds).
+- ``algorithm.off_policy_correction.outlier_token_is_threshold_high``: High threshold for outlier token mask - masks out sequences with any token having importance ratio far outside an acceptable range (low and high thresholds).
+
 Policy Loss Formulation
 ~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -502,7 +566,7 @@ It can be helpful to understand the final loss formulation to see how the differ
       advantages: torch.Tensor,
       config: DictConfig, # trainer.algorithm config
       loss_mask: Optional[torch.Tensor] = None,
-  ) -> torch.Tensor:
+  ) -> Tuple[torch.Tensor, LossMetrics]:
 
       ratio = (log_probs - old_log_probs).exp()
       surr1 = ratio * advantages
@@ -515,7 +579,7 @@ It can be helpful to understand the final loss formulation to see how the differ
         clip_pg_losses2 = torch.min(pg_losses3, clip_pg_losses1)
         loss = torch.where(advantages < 0, clip_pg_losses2, clip_pg_losses1)
       loss = reduce_loss(loss, loss_mask, config.loss_reduction)
-      return loss, clip_ratio
+      return loss, LossMetrics(clip_ratio=clip_ratio)
 
 
 Generator Configuration

skyrl-train/docs/examples/flash_rl.rst

@@ -60,13 +60,13 @@ We highlight some important training parameters configured for FlashRL from our
     DATA_DIR="$HOME/data/dapo"
 
     # TIS parameters
-    USE_TIS=true
+    TIS_TYPE=token
     TIS_IMP_RATIO_CAP=8.0
 
     uv run --isolated --extra flashrl --env-file examples/flash_rl/.env.int8 -m examples.flash_rl.main_dapo_flashrl \
         ...
-        trainer.algorithm.use_tis=$USE_TIS \
-        trainer.algorithm.tis_imp_ratio_cap=$TIS_IMP_RATIO_CAP \
+        trainer.algorithm.off_policy_correction.tis_ratio_type=$TIS_TYPE \
+        trainer.algorithm.off_policy_correction.token_tis_ratio_clip_high=$TIS_IMP_RATIO_CAP \
         generator.sampling_params.logprobs=0 \
         ...
 

skyrl-train/examples/algorithms/custom_policy_loss/main_custom_policy_loss.py

@@ -9,7 +9,7 @@
 from omegaconf import DictConfig
 from skyrl_train.utils import initialize_ray
 from skyrl_train.entrypoints.main_base import BasePPOExp, config_dir, validate_cfg
-from skyrl_train.utils.ppo_utils import PolicyLossRegistry
+from skyrl_train.utils.ppo_utils import PolicyLossRegistry, LossMetrics
 
 
 # Example of custom policy loss: "reinforce"
@@ -27,7 +27,7 @@ def compute_reinforce_policy_loss(
     loss = (-log_probs * advantages).mean()
 
     # Return loss and dummy clip_ratio (no clipping in REINFORCE)
-    return loss, 0.0
+    return loss, LossMetrics(clip_ratio=0.0)
 
 
 # Register the custom policy loss

skyrl-train/examples/flash_rl/run_dapo_gsm8k_flashrl_0.5b_fp8.sh

@@ -33,6 +33,8 @@ MAX_RESPONSE_LENGTH=1024
 
 CKPT_PATH="$HOME/ckpts/gsm8k_0.5B_ckpt"
 
+TIS_TYPE=token
+TIS_IMP_RATIO_CAP=2.0
 
 uv run --isolated --extra flashrl --env-file examples/flash_rl/.env.fp8 --with vllm@https://github.com/NovaSky-AI/SkyRL/releases/download/skyrl_train-v0.1.0/vllm-0.1.dev7509+gcc487699a.d20250821-cp312-cp312-linux_x86_64.whl --with transformers==4.53.3 -- python -m examples.flash_rl.main_dapo_flashrl \
   data.train_data="['$DATA_DIR/train.parquet']" \
@@ -53,8 +55,8 @@ uv run --isolated --extra flashrl --env-file examples/flash_rl/.env.fp8 --with v
   generator.eval_sampling_params.top_p=$EVAL_TOP_P \
   trainer.algorithm.use_kl_loss=$USE_KL_LOSS \
   trainer.algorithm.clip_ratio_c=$CLIP_RATIO_C \
-  trainer.algorithm.use_tis=true \
-  trainer.algorithm.tis_imp_ratio_cap=2.0 \
+  trainer.algorithm.off_policy_correction.tis_ratio_type=$TIS_TYPE \
+  trainer.algorithm.off_policy_correction.token_tis_ratio_clip_high=$TIS_IMP_RATIO_CAP \
   trainer.policy.model.path="Qwen/Qwen2.5-0.5B-Instruct" \
   trainer.placement.colocate_all=true \
   trainer.strategy=fsdp2 \

skyrl-train/examples/flash_rl/run_dapo_gsm8k_flashrl_0.5b_int8.sh

@@ -33,6 +33,9 @@ MAX_RESPONSE_LENGTH=1024
 
 CKPT_PATH="$HOME/ckpts/gsm8k_0.5B_ckpt"
 
+TIS_TYPE=token
+TIS_IMP_RATIO_CAP=2.0
+
 uv run --isolated --extra flashrl --env-file examples/flash_rl/.env.int8 --with vllm@https://github.com/NovaSky-AI/SkyRL/releases/download/skyrl_train-v0.1.0/vllm-0.1.dev7509+gcc487699a.d20250821-cp312-cp312-linux_x86_64.whl --with transformers==4.53.3 -- python -m examples.flash_rl.main_dapo_flashrl \
   data.train_data="['$DATA_DIR/train.parquet']" \
   data.val_data="['$DATA_DIR/validation.parquet']" \
@@ -52,8 +55,8 @@ uv run --isolated --extra flashrl --env-file examples/flash_rl/.env.int8 --with
   trainer.algorithm.dynamic_sampling.max_sample_batches=$DYNAMIC_SAMPLING_MAX_SAMPLE_BATCHES \
   trainer.algorithm.use_kl_loss=$USE_KL_LOSS \
   trainer.algorithm.clip_ratio_c=$CLIP_RATIO_C \
-  trainer.algorithm.use_tis=true \
-  trainer.algorithm.tis_imp_ratio_cap=2.0 \
+  trainer.algorithm.off_policy_correction.tis_ratio_type=$TIS_TYPE \
+  trainer.algorithm.off_policy_correction.token_tis_ratio_clip_high=$TIS_IMP_RATIO_CAP \
   trainer.policy.model.path="Qwen/Qwen2.5-0.5B-Instruct" \
   trainer.placement.colocate_all=true \
   trainer.strategy=fsdp2 \

skyrl-train/examples/flash_rl/run_dapo_gsm8k_flashrl_32b_int8.sh

@@ -33,6 +33,9 @@ MAX_RESPONSE_LENGTH=1024
 
 CKPT_PATH="$HOME/ckpts/gsm8k_32B_ckpt"
 
+TIS_TYPE=token
+TIS_IMP_RATIO_CAP=2.0
+
 uv run --isolated --extra flashrl --env-file examples/flash_rl/.env.int8 --with vllm@https://github.com/NovaSky-AI/SkyRL/releases/download/skyrl_train-v0.1.0/vllm-0.1.dev7509+gcc487699a.d20250821-cp312-cp312-linux_x86_64.whl --with transformers==4.53.3 -m examples.flash_rl.main_dapo_flashrl \
   data.train_data="['$DATA_DIR/train.parquet']" \
   data.val_data="['$DATA_DIR/validation.parquet']" \
@@ -52,8 +55,8 @@ uv run --isolated --extra flashrl --env-file examples/flash_rl/.env.int8 --with
   generator.eval_sampling_params.top_p=$EVAL_TOP_P \
   trainer.algorithm.use_kl_loss=$USE_KL_LOSS \
   trainer.algorithm.clip_ratio_c=$CLIP_RATIO_C \
-  trainer.algorithm.use_tis=true \
-  trainer.algorithm.tis_imp_ratio_cap=2.0 \
+  trainer.algorithm.off_policy_correction.tis_ratio_type=$TIS_TYPE \
+  trainer.algorithm.off_policy_correction.token_tis_ratio_clip_high=$TIS_IMP_RATIO_CAP \
   trainer.policy.model.path="Qwen/Qwen2.5-32B" \
   trainer.placement.colocate_all=true \
   trainer.strategy=fsdp2 \

skyrl-train/examples/flash_rl/run_dapo_repro_flashrl_0.5b_int8.sh

@@ -31,6 +31,8 @@ EVAL_TOP_P=0.7
 CLIP_RATIO_C=10.0
 MAX_RESPONSE_LENGTH=4096
 
+TIS_TYPE=token
+TIS_IMP_RATIO_CAP=2.0
 uv run --isolated --extra flashrl --env-file examples/flash_rl/.env.0.5b_int8 --with vllm@https://github.com/NovaSky-AI/SkyRL/releases/download/skyrl_train-v0.1.0/vllm-0.1.dev7509+gcc487699a.d20250821-cp312-cp312-linux_x86_64.whl --with transformers==4.53.3 -- python -m examples.flash_rl.main_dapo_flashrl \
   data.train_data="['$DATA_DIR/dapo-math-17k-cleaned.parquet']" \
   data.val_data="['$DATA_DIR/aime-2024-cleaned.parquet']" \
@@ -50,8 +52,8 @@ uv run --isolated --extra flashrl --env-file examples/flash_rl/.env.0.5b_int8 --
   trainer.algorithm.dynamic_sampling.max_sample_batches=$DYNAMIC_SAMPLING_MAX_SAMPLE_BATCHES \
   trainer.algorithm.use_kl_loss=$USE_KL_LOSS \
   trainer.algorithm.clip_ratio_c=$CLIP_RATIO_C \
-  trainer.algorithm.use_tis=true \
-  trainer.algorithm.tis_imp_ratio_cap=2.0 \
+  trainer.algorithm.off_policy_correction.tis_ratio_type=$TIS_TYPE \
+  trainer.algorithm.off_policy_correction.token_tis_ratio_clip_high=$TIS_IMP_RATIO_CAP \
   trainer.policy.model.path="Qwen/Qwen2.5-0.5B-Instruct" \
   trainer.placement.colocate_all=true \
   trainer.strategy=fsdp2 \

skyrl-train/examples/flash_rl/run_dapo_repro_flashrl_32b_int8.sh

@@ -33,7 +33,7 @@ MAX_RESPONSE_LENGTH=20480
 MAX_PROMPT_LENGTH=2048
 
 TIS_IMP_RATIO_CAP=8.0
-USE_TIS=true
+TIS_TYPE=token
 LOGPROBS=0
 
 CKPT_PATH="$HOME/ckpts/dapo_32b_ckpt"
@@ -57,8 +57,8 @@ uv run --isolated --extra flashrl --env-file examples/flash_rl/.env.int8 --with
   generator.eval_sampling_params.top_p=$EVAL_TOP_P \
   trainer.algorithm.use_kl_loss=$USE_KL_LOSS \
   trainer.algorithm.clip_ratio_c=$CLIP_RATIO_C \
-  trainer.algorithm.use_tis=$USE_TIS \
-  trainer.algorithm.tis_imp_ratio_cap=$TIS_IMP_RATIO_CAP \
+  trainer.algorithm.off_policy_correction.tis_ratio_type=$TIS_TYPE \
+  trainer.algorithm.off_policy_correction.token_tis_ratio_clip_high=$TIS_IMP_RATIO_CAP \
   trainer.policy.model.path="Qwen/Qwen2.5-32B" \
   trainer.placement.colocate_all=true \
   trainer.strategy=fsdp2 \

skyrl-train/examples/fully_async/async_run_gsm8k.sh

@@ -28,19 +28,19 @@ set -x
 : "${MAX_STALENESS_STEPS:=4}"
 : "${NUM_PARALLEL_GENERATION_WORKERS:=$(( MINI_BATCH_SIZE * (MAX_STALENESS_STEPS + 1) ))}"
 
-USE_TIS=true
+TIS_TYPE=token
 TIS_IMP_RATIO_CAP=2.0
 
-RUN_NAME=gsm8k-async-qwen2.5_1.5B-useTIS_${USE_TIS}-maxStale${MAX_STALENESS_STEPS}-numCon${NUM_PARALLEL_GENERATION_WORKERS}-${NUM_POLICY_GPUS}train${NUM_INFERENCE_GPUS}gen
+RUN_NAME=gsm8k-async-qwen2.5_1.5B-TIS_TYPE_${TIS_TYPE}-maxStale${MAX_STALENESS_STEPS}-numCon${NUM_PARALLEL_GENERATION_WORKERS}-${NUM_POLICY_GPUS}train${NUM_INFERENCE_GPUS}gen
 
 uv run --isolated --extra $INFERENCE_BACKEND -m examples.fully_async.main_async \
   data.train_data="['$DATA_DIR/train.parquet']" \
   data.val_data="['$DATA_DIR/validation.parquet']" \
   trainer.fully_async.max_staleness_steps=${MAX_STALENESS_STEPS} \
   trainer.fully_async.num_parallel_generation_workers=${NUM_PARALLEL_GENERATION_WORKERS} \
   trainer.algorithm.advantage_estimator="grpo" \
-  trainer.algorithm.use_tis=$USE_TIS \
-  trainer.algorithm.tis_imp_ratio_cap=$TIS_IMP_RATIO_CAP \
+  trainer.algorithm.off_policy_correction.tis_ratio_type=$TIS_TYPE \
+  trainer.algorithm.off_policy_correction.token_tis_ratio_clip_high=$TIS_IMP_RATIO_CAP \
   trainer.policy.model.path="Qwen/Qwen2.5-1.5B-Instruct" \
   trainer.placement.colocate_all=false \
   trainer.strategy=fsdp2 \

skyrl-train/examples/megatron/run_megatron_dapo_qwen3_30b_a3b.sh

@@ -53,7 +53,7 @@ MEGATRON_ETP=1
 
 # TIS parameters
 TIS_IMP_RATIO_CAP=2.0
-USE_TIS=true
+TIS_TYPE=token
 
 uv run --isolated --extra mcore -m examples.algorithms.dapo.main_dapo \
   data.train_data="['$TRAIN_FILE']" \
@@ -83,8 +83,8 @@ uv run --isolated --extra mcore -m examples.algorithms.dapo.main_dapo \
   trainer.policy.megatron_config.context_parallel_size=$MEGATRON_CP \
   trainer.policy.megatron_config.expert_model_parallel_size=$MEGATRON_EP \
   trainer.policy.megatron_config.expert_tensor_parallel_size=$MEGATRON_ETP \
-  trainer.algorithm.use_tis=$USE_TIS \
-  trainer.algorithm.tis_imp_ratio_cap=$TIS_IMP_RATIO_CAP \
+  trainer.algorithm.off_policy_correction.tis_ratio_type=$TIS_TYPE \
+  trainer.algorithm.off_policy_correction.token_tis_ratio_clip_high=$TIS_IMP_RATIO_CAP \
   trainer.epochs=20 \
   trainer.algorithm.eps_clip_low=$CLIP_RATIO_LOW \
   trainer.algorithm.eps_clip_high=$CLIP_RATIO_HIGH \

skyrl-train/examples/megatron/run_megatron_dapo_qwen3_30b_a3b_lora.sh

@@ -41,7 +41,7 @@ MINI_BATCH_SIZE=32
 N_SAMPLES_PER_PROMPT=16
 EVAL_N_SAMPLES_PER_PROMPT=32
 ENFORCE_EAGER=true # cuda graphs can cause some instability
-LR=3e-5
+LR=1e-5 # 10x compared to full finetuning
 
 # megatron config
 MEGATRON_TP=4
@@ -51,12 +51,12 @@ MEGATRON_EP=8
 MEGATRON_ETP=1
 
 # lora config
-LORA_RANK=32
-LORA_ALPHA=64
+LORA_RANK=128
+LORA_ALPHA=128
 
-# TIS parameters
+# rollout correction parameters
+TIS_RATIO_TYPE="token"
 TIS_IMP_RATIO_CAP=2.0
-USE_TIS=true
 
 uv run --isolated --extra mcore -m examples.algorithms.dapo.main_dapo \
   data.train_data="['$TRAIN_FILE']" \
@@ -88,8 +88,8 @@ uv run --isolated --extra mcore -m examples.algorithms.dapo.main_dapo \
   trainer.policy.megatron_config.expert_tensor_parallel_size=$MEGATRON_ETP \
   trainer.policy.model.lora.rank=$LORA_RANK \
   trainer.policy.model.lora.alpha=$LORA_ALPHA \
-  trainer.algorithm.use_tis=$USE_TIS \
-  trainer.algorithm.tis_imp_ratio_cap=$TIS_IMP_RATIO_CAP \
+  trainer.algorithm.off_policy_correction.tis_ratio_type=$TIS_RATIO_TYPE \
+  trainer.algorithm.off_policy_correction.token_tis_ratio_clip_high=$TIS_IMP_RATIO_CAP \
   trainer.epochs=20 \
   trainer.algorithm.eps_clip_low=$CLIP_RATIO_LOW \
   trainer.algorithm.eps_clip_high=$CLIP_RATIO_HIGH \

skyrl-train/examples/megatron/run_megatron_dapo_qwen3_4b.sh

@@ -50,7 +50,7 @@ MEGATRON_ETP=null
 
 # TIS parameters
 TIS_IMP_RATIO_CAP=2.0
-USE_TIS=true
+TIS_TYPE=token
 
 uv run --isolated --extra mcore -m examples.algorithms.dapo.main_dapo \
   data.train_data="['$TRAIN_FILE']" \
@@ -80,8 +80,8 @@ uv run --isolated --extra mcore -m examples.algorithms.dapo.main_dapo \
   trainer.policy.megatron_config.context_parallel_size=$MEGATRON_CP \
   trainer.policy.megatron_config.expert_model_parallel_size=$MEGATRON_EP \
   trainer.policy.megatron_config.expert_tensor_parallel_size=$MEGATRON_ETP \
-  trainer.algorithm.use_tis=$USE_TIS \
-  trainer.algorithm.tis_imp_ratio_cap=$TIS_IMP_RATIO_CAP \
+  trainer.algorithm.off_policy_correction.tis_ratio_type=$TIS_TYPE \
+  trainer.algorithm.off_policy_correction.token_tis_ratio_clip_high=$TIS_IMP_RATIO_CAP \
   trainer.epochs=20 \
   trainer.algorithm.eps_clip_low=$CLIP_RATIO_LOW \
   trainer.algorithm.eps_clip_high=$CLIP_RATIO_HIGH \