diff --git a/.gitignore b/.gitignore
index 2a9250276..f0d64d70f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -159,3 +159,4 @@ dmypy.json
 cache/
 local_dataset_cache/
 scratch/
+vllm_olmo2.5/
diff --git a/Dockerfile b/Dockerfile
index 2f6dae3c4..3a0efe63e 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -65,6 +65,8 @@ ENV UV_CACHE_DIR=/root/.cache/uv
 ENV HF_HUB_ENABLE_HF_TRANSFER=1
 ENV UV_COMPILE_BYTECODE=0
 
+RUN git clone -b shanea/olmo2-retrofit https://github.com/2015aroras/vllm.git vllm_olmo2.5
+
 # Install dependencies
 RUN --mount=type=cache,target=${UV_CACHE_DIR} \
     --mount=type=bind,source=uv.lock,target=uv.lock \
@@ -78,7 +80,7 @@ COPY configs configs
 COPY scripts scripts
 COPY mason.py mason.py
 # Copy oe-eval-internal if it exists (wildcard pattern won't fail if missing)
-COPY oe-eval-interna[l] oe-eval-internal/
+COPY oe-eval-internal oe-eval-internal
 COPY open_instruct open_instruct
 
 # Add build arguments for git information
diff --git a/Makefile b/Makefile
index 55ce7d63e..fc76a298a 100644
--- a/Makefile
+++ b/Makefile
@@ -1,4 +1,4 @@
-.PHONY: style quality
+.PHONY: style quality docker
 
 # make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
 export PYTHONPATH = open_instruct
@@ -16,3 +16,13 @@ style-check:   ## *fail* if anything needs rewriting
 
 quality-check: ## *fail* if any rewrite was needed
 	uv run ruff check --exit-non-zero-on-fix $(check_dirs)
+
+setup:
+	git clone -b shanea/olmo2-retrofit https://github.com/2015aroras/vllm.git vllm_olmo2.5
+
+docker:
+	DOCKER_BUILDKIT=1 docker build -f Dockerfile --build-arg UV_CACHE_DIR=$(UV_CACHE_DIR) -t open_instruct_2.5-rl0 .
+	# if you are internally at AI2, you can create an image like this:
+	$(eval beaker_user := $(shell beaker account whoami --format json | jq -r '.[0].name'))
+	beaker image delete $(beaker_user)/open_instruct_2.5-rl0
+	beaker image create open_instruct_2.5-rl0 -n open_instruct_2.5-rl0 -w ai2/$(beaker_user)
diff --git a/generate_olmo25.sh b/generate_olmo25.sh
new file mode 100755
index 000000000..ea80ed685
--- /dev/null
+++ b/generate_olmo25.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+
+MODEL_NAME_OR_PATH="/weka/oe-training-default/ai2-llm/checkpoints/tylerr/long-context/olmo25_7b_lc_64k_6T_M100B_round5-sparkle_6634-pre_s2pdf_gzip2080_cweN-yake-all-olmo_packing_yarn-fullonly_50B-fb13a737/step11921-hf"
+# DATASET="mnoukhov/DAPO-Math-14k-Processed-RLVR"
+DATASET="TTTXXX01/MATH_3000_Filtered"
+EXP_NAME="generate_olmo25_teng3k"
+
+python mason.py \
+    --task_name ${EXP_NAME} \
+    --cluster ai2/jupiter \
+    --image ${1:-michaeln/open_instruct_olmo2_retrofit} \
+    --workspace ai2/tulu-thinker \
+    --priority high \
+    --pure_docker_mode \
+    --preemptible \
+    --gpus 2 \
+    --num_nodes 1 \
+    --max_retries 0 \
+    --budget ai2/oe-adapt \
+    --env VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
+    --env VLLM_ATTENTION_BACKEND="FLASH_ATTN" \
+    -- \
+python scripts/data/rlvr/filtering_vllm.py \
+    --model $MODEL_NAME_OR_PATH \
+    --dataset $DATASET \
+    --split train \
+    --temperature 0.7 \
+    --top_p 0.95 \
+    --offset 0 \
+    --size 100000 \
+    --chat_template olmo_thinker_r1_style_nochat \
+    --output-file filtered_datasets/olmo25_7b_lc_dapo.jsonl \
+    --number_samples 16
diff --git a/open_instruct/dataset_transformation.py b/open_instruct/dataset_transformation.py
index 3873f073c..9dd430eee 100644
--- a/open_instruct/dataset_transformation.py
+++ b/open_instruct/dataset_transformation.py
@@ -442,6 +442,33 @@ def visualize_token_role(tokens: list[int], masks: list[int], tokenizer: PreTrai
         "{% endif %}"
         "{% endfor %}"
     ),
+    "olmo_thinker_r1_style_nochat": (
+        "Solve the following math problem step by step. "
+        "Reason about the question in <think> </think> tags "
+        "then provide the final answer in <answer> </answer> tags "
+        "so the full response is <think> reasoning process here </think> "
+        "<answer> answer here </answer>."
+        "\n\n"
+        "{% for message in messages %}"
+        "{{ '\n\n' if not loop.first else '' }}"
+        "{{ message['content'] + '\n' }}"
+        "{% if loop.last and add_generation_prompt %}"
+        "{{ 'Solving step by step\n<think>' }}"
+        "{% endif %}"
+        "{% endfor %}"
+    ),
+    "olmo_thinker_dapo": (
+        "Solve the following math problem step by step. "
+        "The last line of your response should be the answer to the problem in form Answer: $Answer (without quotes) where $Answer is the answer to the problem."
+        "\n\n"
+        "{% for message in messages %}"
+        "{{ '\n\n' if not loop.first else '' }}"
+        "{{ message['content'] + '\n' }}"
+        "{% if loop.last and add_generation_prompt %}"
+        "{{ '\nRemember to put your answer on its own line after \"Answer:\"' }}"
+        "{% endif %}"
+        "{% endfor %}"
+    ),
     # template is taken from https://arxiv.org/abs/2501.12948.
     "r1_simple_chat": (
         "A conversation between User and Assistant. "
diff --git a/open_instruct/grpo_fast.py b/open_instruct/grpo_fast.py
index aca26ded9..4a06496e0 100644
--- a/open_instruct/grpo_fast.py
+++ b/open_instruct/grpo_fast.py
@@ -409,6 +409,8 @@ class Args:
     """the max generation length for evaluation for oe-eval"""
     oe_eval_beaker_image: Optional[str] = None
     """the docker image for evaluation for oe-eval"""
+    oe_eval_gpu_multiplier: Optional[int] = 1
+    """gpu mulitplier for eval jobs"""
     eval_priority: Literal["low", "normal", "high", "urgent"] = "normal"
     """the priority of auto-launched evaluation jobs"""
 
@@ -452,10 +454,10 @@ def __post_init__(self):
             "At least one reward must be applied!"
         )
         # Ensure we have enough prompts for all VLLM engines
-        if self.num_unique_prompts_rollout < self.vllm_num_engines:
-            raise ValueError(
-                f"{self.num_unique_prompts_rollout=} must be >= {self.vllm_num_engines=} to avoid empty batches."
-            )
+        # if self.num_unique_prompts_rollout < self.vllm_num_engines:
+        #     raise ValueError(
+        #         f"{self.num_unique_prompts_rollout=} must be >= {self.vllm_num_engines=} to avoid empty batches."
+        #     )
         # Initialize stop_strings if None
         if self.stop_strings is None:
             self.stop_strings = []
@@ -560,16 +562,21 @@ def __init__(self, data: np.ndarray, batch_size: int, seed: Optional[int] = None
         self.index = 0
         self.rng = np.random.default_rng(seed)
         self.rng.shuffle(self.data)
+        self.exclude_list = []
 
         # Ensure the effective dataset size is divisible by batch_size
-        self.effective_size = len(self.data) - (len(self.data) % batch_size)
+        self._update_effective_size()
 
     def __iter__(self) -> Iterator[List[int]]:
         return self
 
     def __next__(self) -> List[int]:
+        if self.effective_size == 0:
+            raise StopIteration("No data available to sample from the iterator.")
+
         if self.index >= self.effective_size:
             self.index = 0
+            self._update_effective_size()
             self.rng.shuffle(self.data)
 
         end_index = self.index + self.batch_size
@@ -578,6 +585,10 @@ def __next__(self) -> List[int]:
 
         return batch
 
+    def exclude_indices(self, exclude_list: List) -> None:
+        """Exclude provided data points from future sampling."""
+        self.exclude_list.extend(exclude_list)
+
     def get_state(self) -> Dict[str, Any]:
         """Get the current state of the iterator for checkpointing."""
         return {
@@ -591,6 +602,17 @@ def set_state(self, state: Dict[str, Any]) -> None:
         self.index = state["index"]
         self.data = state["data"].copy()
         self.rng.bit_generator.state = state["rng_state"]
+        self._update_effective_size()
+
+    def _update_effective_size(self) -> None:
+        if self.exclude_list:
+            mask = ~np.isin(self.data, self.exclude_list)
+            if mask.all():
+                return
+
+            self.data = self.data[mask]
+            self.exclude_list = []
+        self.effective_size = len(self.data) - (len(self.data) % self.batch_size)
 
 
 @ray.remote(num_gpus=1)
@@ -1322,6 +1344,7 @@ def launch_ai2_evals_on_weka_wrapper(self, step_dir, leaderboard_name, wandb_url
                 args.gs_bucket_path,
                 args.eval_priority,
                 args.oe_eval_beaker_image,
+                args.oe_eval_gpu_multiplier,
             )
 
 
@@ -1569,6 +1592,7 @@ def accumulate_inference_batches(
     all_ground_truths = []
     all_datasets = []
     all_raw_queries = []
+    all_indices = []
     for i in tqdm(
         range(num_prompts),
         total=num_prompts,
@@ -1595,6 +1619,7 @@ def accumulate_inference_batches(
         all_ground_truths.append(ground_truth)
         all_datasets.append(dataset)
         all_raw_queries.append(raw_query)
+        all_indices.append(result.dataset_index)
 
     # Combine all results into a single GenerationResult
     combined_responses = []
@@ -1675,13 +1700,13 @@ def accumulate_inference_batches(
     if actor_manager is not None:
         ray.get(actor_manager.report_token_statistics.remote(accumulated_stats))
 
-    # Note: We don't have dataset_indices here, but they're not needed for the returned batch
+    # Preserve dataset indices so downstream filtering keeps track of original prompts
     batch = Batch(
         queries=all_queries,
         ground_truths=all_ground_truths,
         datasets=all_datasets,
         raw_queries=all_raw_queries,
-        indices=None,  # Not meaningful for combined results
+        indices=all_indices,
     )
     return combined_result, batch, prompt_lengths, response_lengths
 
@@ -1698,6 +1723,7 @@ def data_preparation_thread(
     resume_training_step: int,
     actor_manager=None,
     model_dims: utils.ModelDims = None,
+    train_iterator: ShufflingIterator = None,
 ):
     for training_step in range(resume_training_step, num_training_steps + 1):
         # Streaming accumulation: collect results as they arrive
@@ -1778,6 +1804,7 @@ def data_preparation_thread(
                 max_possible_score += args.verification_reward
             if args.apply_r1_style_format_reward and args.additive_format_reward:
                 max_possible_score += args.r1_style_format_reward
+
             unsolved_batch_size_ratio = ((scores != max_possible_score) > 0).sum() / len(scores)
             # In GRPO, if the std of grouped rewards is 0, then there is zero gradient for the batch
             # of args.num_samples_per_prompt_rollout responses, so we need to filter out those batches
@@ -1869,9 +1896,11 @@ def data_preparation_thread(
 
             # Count groups with all zero rewards
             all_zero_groups = (scores_per_prompt == 0).all(axis=-1).sum()
+            all_solved_groups = (scores_per_prompt == max_possible_score).all(axis=-1).sum()
             total_groups = len(scores_per_prompt)
             logger.info(
                 f"[Reward Summary] Groups with all zero rewards: {all_zero_groups}/{total_groups} "
+                f" Groups with all solved rewards: {all_solved_groups}/{total_groups} "
                 f"({all_zero_groups / total_groups:.1%})"
             )
 
@@ -1996,6 +2025,7 @@ def data_preparation_thread(
 
             # Use the already calculated reward summary metrics for wandb
             all_zero_groups_ratio = all_zero_groups / total_groups if total_groups > 0 else 0
+            all_solved_groups_ratio = all_solved_groups / total_groups if total_groups > 0 else 0
 
             metrics = {
                 "scores": np.array(scores).mean(),
@@ -2004,6 +2034,8 @@ def data_preparation_thread(
                 "packed_ratio": len(packed_sequences.query_responses) / len(responses) if len(responses) > 0 else 0,
                 "val/all_zero_reward_groups": all_zero_groups,
                 "val/all_zero_reward_groups_ratio": all_zero_groups_ratio,
+                "val/all_solved_reward_groups": all_solved_groups,
+                "val/all_solved_reward_groups_ratio": all_solved_groups_ratio,
                 "val/total_reward_groups": total_groups,
                 "val/sequence_lengths": sequence_lengths.mean(),
                 "val/sequence_lengths_min": sequence_lengths.min(),
@@ -2453,7 +2485,7 @@ def one_training_step(
             )
 
     save_time = 0
-    if args.save_freq > 0 and training_step % args.save_freq == 0 and (args.eval_on_step_0 or training_step > 1):
+    if args.save_freq > 0 and (training_step % args.save_freq == 0 or (training_step == 1 and args.eval_on_step_0)):
         with Timer("[Main Thread] 🗡️ Saving model") as timer:
             checkpoint_dir = f"{args.output_dir}_checkpoints"
             step_dir = os.path.join(checkpoint_dir, f"step_{training_step}")
@@ -2864,6 +2896,7 @@ def run_training(
         resume_training_step,
         actor_manager,
         model_dims,
+        iter_dataloader,
     )
 
     def health_check_fn():
diff --git a/open_instruct/utils.py b/open_instruct/utils.py
index be5d40099..af42df13a 100644
--- a/open_instruct/utils.py
+++ b/open_instruct/utils.py
@@ -1145,6 +1145,7 @@ def launch_ai2_evals_on_weka(
     gs_bucket_path: Optional[str] = None,
     eval_priority: Optional[str] = "normal",
     beaker_image: Optional[str] = None,
+    oe_eval_gpu_multiplier: Optional[int] = 1,
 ) -> None:
     weka_cluster = "ai2/saturn ai2/neptune"
     gcp_cluster = "ai2/augusta"
@@ -1174,6 +1175,7 @@ def launch_ai2_evals_on_weka(
 
     command = f"""\
 python scripts/submit_eval_jobs.py \
+--gpu_multiplier {oe_eval_gpu_multiplier} \
 --model_name {leaderboard_name} \
 --location {path} \
 --cluster {cluster} \
diff --git a/pyproject.toml b/pyproject.toml
index 5ec2c83b3..b1b599208 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,17 +19,17 @@ dependencies = [
     "nvitop>=1.4.2",
     "packaging>=24.2",
     "peft>=0.13.2",
-    "ray[default]>=2.44.1",
+    "ray[default]==2.46.0",
     "setuptools>=75.6.0,<80.0.0",
     "tensorboard>=2.18.0",
     "torch>=2.7.0,<2.8",
-    "transformers>=4.52.4,<4.54.0", # see https://github.com/vllm-project/vllm-ascend/issues/2046
-    "vllm==0.9.1",
+    "transformers @ git+https://github.com/2015aroras/transformers.git@shanea/olmo2-retrofit",
     "wandb==0.18.1",
     "langdetect==1.0.9",
     "immutabledict==1.2.0",
     "flash-attn>=2.8.0.post2; platform_system != 'Darwin'",
     "liger-kernel>=0.5.4; platform_system != 'Darwin'",
+    "vllm" # installed locally with git clone because otherwise errors
 ]
 
 [build-system]
@@ -44,12 +44,14 @@ flash-attn = [{ requirement = "torch", match-runtime = true }]
 
 [tool.uv.extra-build-variables]
 flash-attn = { FLASH_ATTENTION_SKIP_CUDA_BUILD = "TRUE" }
+vllm = { VLLM_USE_PRECOMPILED = "1" }
 
 # pytorch related setups
 [tool.uv.sources]
 torch = [
   { index = "pytorch-cu128", marker = "platform_system != 'Darwin'"},
 ]
+vllm = { path = "vllm_olmo2.5", editable = true }
 
 [[tool.uv.index]]
 name = "pytorch-cu128"
@@ -95,6 +97,7 @@ target-version = ['py310']
 
 [tool.isort]
 known_first_party = ["open_instruct"]
+known-third-party = ["wandb"]
 profile = "black"
 src_paths = ["open_instruct"]
 
diff --git a/scripts/data/rlvr/filtering_vllm.py b/scripts/data/rlvr/filtering_vllm.py
index 160dca91f..bf8c0d700 100644
--- a/scripts/data/rlvr/filtering_vllm.py
+++ b/scripts/data/rlvr/filtering_vllm.py
@@ -1,4 +1,4 @@
-'''
+"""
 python mason.py \
   --cluster ai2/jupiter --image nathanl/open_instruct_auto \
   --workspace ai2/tulu-thinker \
@@ -15,7 +15,8 @@
   --size 100000 \
   --output-file filtered_datasets/qwen2_5_openthoughts2/orz.jsonl \
   --number_samples 8
-'''
+"""
+
 import argparse
 import json
 
@@ -27,65 +28,20 @@
 
 
 def main():
-    parser = argparse.ArgumentParser(
-        description="Bulk-generate N samples per HF dataset record using vLLM."
-    )
-    parser.add_argument(
-        "--model",
-        required=True,
-        help="vLLM model ID (e.g. facebook/opt-125m)"
-    )
-    parser.add_argument(
-        "--dataset",
-        required=True,
-        help="HF dataset name (e.g. squad)"
-    )
+    parser = argparse.ArgumentParser(description="Bulk-generate N samples per HF dataset record using vLLM.")
+    parser.add_argument("--model", required=True, help="vLLM model ID (e.g. facebook/opt-125m)")
+    parser.add_argument("--dataset", required=True, help="HF dataset name (e.g. squad)")
+    parser.add_argument("--split", default="train", help="Which split to load")
+    parser.add_argument("--offset", type=int, required=True, help="Start index into the split")
+    parser.add_argument("--size", type=int, required=True, help="Number of records to process")
+    parser.add_argument("--output-file", default=None, help="Path for output JSONL")
     parser.add_argument(
-        "--split",
-        default="train",
-        help="Which split to load"
-    )
-    parser.add_argument(
-        "--offset",
-        type=int,
-        required=True,
-        help="Start index into the split"
-    )
-    parser.add_argument(
-        "--size",
-        type=int,
-        required=True,
-        help="Number of records to process"
-    )
-    parser.add_argument(
-        "--output-file",
-        default=None,
-        help="Path for output JSONL"
-    )
-    parser.add_argument(
-        "--push_to_hub",
-        default=None,
-        type=str,
-        help="Give a dataset name to push this data to the hub."
-    )
-    parser.add_argument(
-        "--chat_template",
-        type=str,
-        default=None,
-        help="Chat template name"
-    )
-    parser.add_argument(
-        "--number_samples",
-        type=int,
-        default=8,
-        help="Number of samples to generate per record"
-    )
-    parser.add_argument(
-        "--temperature",
-        type=float,
-        default=1.0,
-        help="Sampling temperature"
+        "--push_to_hub", default=None, type=str, help="Give a dataset name to push this data to the hub."
     )
+    parser.add_argument("--chat_template", type=str, default=None, help="Chat template name")
+    parser.add_argument("--number_samples", type=int, default=8, help="Number of samples to generate per record")
+    parser.add_argument("--temperature", type=float, default=1.0, help="Sampling temperature")
+    parser.add_argument("--top_p", type=float, default=1.0, help="Sampling temperature")
     args = parser.parse_args()
 
     # 1. Load and slice dataset
@@ -106,20 +62,14 @@ def main():
         tokenizer.apply_chat_template(
             sample["messages"][:-1] if len(sample["messages"]) > 1 else sample["messages"],
             add_generation_prompt=True,
-            tokenize=False
+            tokenize=False,
         )
         for sample in subset
     ]
     # 4. vLLM bulk generate
-    llm = LLM(
-        model=args.model,
-        dtype="bfloat16",
-        enable_prefix_caching=True
-    )
+    llm = LLM(model=args.model, dtype="bfloat16", enable_prefix_caching=True)
     sampling_params = SamplingParams(
-        temperature=args.temperature,
-        n=args.number_samples,
-        max_tokens=32768,
+        temperature=args.temperature, top_p=args.top_p, n=args.number_samples, max_tokens=32768
     )
     outputs = llm.generate(prompts, sampling_params)
 
diff --git a/scripts/train/build_image_and_launch.sh b/scripts/train/build_image_and_launch.sh
index 98ce2dc6f..55c0b1994 100755
--- a/scripts/train/build_image_and_launch.sh
+++ b/scripts/train/build_image_and_launch.sh
@@ -21,7 +21,7 @@ git_branch=$(git rev-parse --abbrev-ref HEAD)
 # Sanitize the branch name to remove invalid characters for Beaker names
 # Beaker names can only contain letters, numbers, -_. and may not start with -
 sanitized_branch=$(echo "$git_branch" | sed 's/[^a-zA-Z0-9._-]/-/g' | sed 's/^-//')
-image_name=open-instruct-integration-test-${sanitized_branch}
+image_name=open-instruct-integration-test-${sanitized_branch}-${git_hash}
 
 beaker_user=$(beaker account whoami --format json | jq -r '.[0].name')
 
diff --git a/scripts/train/debug/grpo_fast.sh b/scripts/train/debug/grpo_fast.sh
index 8e16356a9..6b0d0d183 100755
--- a/scripts/train/debug/grpo_fast.sh
+++ b/scripts/train/debug/grpo_fast.sh
@@ -1,3 +1,19 @@
+#!/bin/bash
+
+python mason.py \
+    --task_name grpo_debug_small \
+    --cluster ai2/augusta \
+    --workspace ai2/oe-adapt-code \
+    --priority high \
+    --pure_docker_mode \
+    --image michaeln/open_instruct_2.5-rl0 \
+    --preemptible \
+    --num_nodes 1 \
+    --env VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
+    --env VLLM_ATTENTION_BACKEND="FLASH_ATTN" \
+    --gpus 1 \
+    --budget ai2/oe-adapt \
+    -- \
 uv run python open_instruct/grpo_fast.py \
     --dataset_mixer_list ai2-adapt-dev/rlvr_gsm8k_zs 64 \
     --dataset_mixer_list_splits train \
@@ -18,19 +34,25 @@ uv run python open_instruct/grpo_fast.py \
     --ground_truths_key ground_truth \
     --chat_template_name r1_simple_chat_postpend_think \
     --learning_rate 3e-7 \
-    --total_episodes 200 \
+    --total_episodes 1600 \
     --deepspeed_stage 2 \
     --num_epochs 1 \
     --num_learners_per_node 1 \
     --vllm_tensor_parallel_size 1 \
-    --beta 0.01 \
+    --beta 0. \
     --seed 3 \
-    --local_eval_every 1 \
+    --local_eval_every 25 \
     --vllm_sync_backend gloo \
     --vllm_gpu_memory_utilization 0.3 \
-    --save_traces \
     --vllm_enforce_eager \
     --gradient_checkpointing \
     --single_gpu_mode \
     --push_to_hub false \
-    # --with_tracking
+    --with_tracking \
+    --save_freq 25 \
+    --eval_on_step_0 \
+    --oe_eval_max_length 512 \
+    --try_launch_beaker_eval_jobs_on_weka True \
+    --oe_eval_tasks gsm8k \
+    --oe_eval_beaker_image michaeln/oe_eval_olmo2_retrofit \
+    --eval_priority high
diff --git a/scripts/train/rlvr/grpo_olmo25.sh b/scripts/train/rlvr/grpo_olmo25.sh
new file mode 100755
index 000000000..049b6f29d
--- /dev/null
+++ b/scripts/train/rlvr/grpo_olmo25.sh
@@ -0,0 +1,88 @@
+#!/bin/bash
+
+# OLMo 2.5 model
+MODEL_NAME_OR_PATH="/weka/oe-training-default/ai2-llm/checkpoints/tylerr/long-context/olmo25_7b_lc_64k_6T_M100B_round5-sparkle_6634-pre_s2pdf_gzip2080_cweN-yake-all-olmo_packing_yarn-fullonly_50B-fb13a737/step11921-hf"
+GS_MODEL_NAME="olmo25_7b_lc_final_fb13a737"
+
+# english only DAPO
+# DATASETS="mnoukhov/DAPO-Math-14k-Processed-RLVR 1.0 TTTXXX01/MATH_3000_Filtered 1.0"
+DATASETS="saurabh5/DAPO-Math-17k-Processed_filtered_olmo_completions_new_template_filtered 1.0 saurabh5/MATH_3000_Filtered_olmo_completions_new_template_filtered 1.0"
+# DATASETS="mnoukhov/deepscaler_20k_medhard_nolatex_rlvr 1.0"
+# DATASETS=""
+
+# math evals
+# EVALS="minerva_math_500::hamish_zs_reasoning_deepseek"
+EVALS="aime:zs_cot_r1::pass_at_32_2024_dapo,aime:zs_cot_r1::pass_at_32_2025_dapo"
+
+# AIME 2024, 2025 local evals
+LOCAL_EVALS="mnoukhov/aime2024-25-rlvr 1.0 mnoukhov/aime2024-25-rlvr 1.0"
+LOCAL_EVAL_SPLITS="test_2024 test_2024 test_2025 test_2025"
+# tengmath3k
+# EXP_NAME="grpo_deepscaler20k_k8_${GS_MODEL_NAME}"
+EXP_NAME="grpo_17kfilter_${GS_MODEL_NAME}"
+
+cluster=ai2/augusta
+
+python mason.py \
+    --task_name ${EXP_NAME} \
+    --cluster ${cluster} \
+    --workspace ai2/olmo-instruct \
+    --priority urgent \
+    --pure_docker_mode \
+    --image michaeln/open_instruct_2.5-rl0 \
+    --preemptible \
+    --num_nodes 9 \
+    --env VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
+    --env VLLM_ATTENTION_BACKEND="FLASH_ATTN" \
+    --gs_model_name $GS_MODEL_NAME \
+    --gpus 8 \
+    --budget ai2/oe-adapt \
+    -- \
+source configs/beaker_configs/ray_node_setup.sh \&\& \
+source configs/beaker_configs/code_api_setup.sh \&\& \
+python open_instruct/grpo_fast.py \
+    --exp_name ${EXP_NAME} \
+    --beta 0.0 \
+    --async_steps 4 \
+    --inflight_updates \
+    --truncated_importance_sampling_ratio_cap 2.0 \
+    --num_samples_per_prompt_rollout 8 \
+    --num_unique_prompts_rollout 32 \
+    --num_mini_batches 1 \
+    --learning_rate 1e-6 \
+    --per_device_train_batch_size 1 \
+    --kl_estimator kl3 \
+    --dataset_mixer_list $DATASETS \
+    --dataset_mixer_list_splits train \
+    --dataset_mixer_eval_list $LOCAL_EVALS \
+    --dataset_mixer_eval_list_splits $LOCAL_EVAL_SPLITS \
+    --max_token_length 2048 \
+    --max_prompt_token_length 2048 \
+    --response_length 16384 \
+    --pack_length 32768 \
+    --model_name_or_path ${MODEL_NAME_OR_PATH} \
+    --chat_template_name olmo_thinker_dapo \
+    --non_stop_penalty False \
+    --temperature 1.0 \
+    --total_episodes 256000 \
+    --deepspeed_stage 3 \
+    --num_learners_per_node 8 \
+    --vllm_num_engines 64 \
+    --vllm_tensor_parallel_size 1 \
+    --lr_scheduler_type constant \
+    --apply_verifiable_reward true \
+    --seed 1 \
+    --local_eval_every 100 \
+    --save_freq 100 \
+    --checkpoint_state_freq 100 \
+    --gradient_checkpointing \
+    --with_tracking \
+    --vllm_enable_prefix_caching \
+    --clip_higher 0.272 \
+    --mask_truncated_completions True \
+    --oe_eval_max_length 32768 \
+    --try_launch_beaker_eval_jobs_on_weka True \
+    --eval_priority high \
+    --oe_eval_tasks $EVALS \
+    --oe_eval_gpu_multiplier 4 \
+    --oe_eval_beaker_image michaeln/oe_eval_olmo2_retrofit
diff --git a/uv.lock b/uv.lock
index e6c858c79..fc187d904 100644
--- a/uv.lock
+++ b/uv.lock
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