diff --git a/apps/grpo/qwen3_30b_a3.yaml b/apps/grpo/qwen3_30b_a3.yaml
new file mode 100644
index 000000000..48f86ccc2
--- /dev/null
+++ b/apps/grpo/qwen3_30b_a3.yaml
@@ -0,0 +1,143 @@
+# Grouped Relative Policy Optimization (GRPO)
+# >>> python -m apps.grpo.main --config apps/grpo/qwen32b.yaml
+# NOTE - This has not been tested for correctness yet! All testing so far has been only for infrastructure stability
+# Global configuration
+group_size: 16
+local_batch_size: 2 # per-device batch size
+max_req_tokens: 1024
+max_res_tokens: 1024
+model: "Qwen/Qwen3-32B"
+off_by_n: 1 # Off by one by default
+provisioner:
+  launcher: slurm
+# Main loop configuration
+rollout_threads: 4 # make this 4x the number of policy replicas seems to work well
+# Observability configuration
+metric_logging:
+  wandb:
+    project: "grpo-training"
+    group: "grpo_exp_${oc.env:USER}"
+    reduce_across_ranks: True
+  console:
+    reduce_across_ranks: True
+# Dataset configuration
+dataset:
+  path: "openai/gsm8k"
+  revision: "main"
+  data_split: "train"
+  streaming: true
+  model: ${model}
+# Policy configuration
+policy:
+  engine_args:  # https://docs.vllm.ai/en/v0.10.0/api/vllm/engine/arg_utils.html#vllm.engine.arg_utils.EngineArgs
+    model: ${model}
+    tensor_parallel_size: 4
+    pipeline_parallel_size: 1
+    enforce_eager: false
+  sampling_params:  # https://docs.vllm.ai/en/v0.10.0/api/vllm/sampling_params.html#vllm.sampling_params.SamplingParams
+    n: ${group_size}
+    max_tokens: ${max_res_tokens}
+    temperature: 1.0
+    top_p: 1.0
+# Trainer configuration
+trainer:
+  model:
+    name: qwen3
+    flavor: 32B
+    hf_assets_path: hf://${model}
+  optimizer:
+    name: AdamW
+    lr: 1e-5
+    eps: 1e-8
+  lr_scheduler:
+    warmup_steps: 1
+  training:
+    local_batch_size: ${local_batch_size}
+    seq_len: 2048
+    max_norm: 1.0
+    steps: 1000000
+    dtype: bfloat16
+    gc_freq: 1
+  compile:
+    enable: false
+  parallelism:
+    data_parallel_replicate_degree: 1
+    data_parallel_shard_degree: -1
+    tensor_parallel_degree: 1
+    pipeline_parallel_degree: 1
+    context_parallel_degree: 1
+    expert_parallel_degree: 1
+    disable_loss_parallel: true
+  checkpoint:
+    enable: true
+    initial_load_path: hf://${model}
+    initial_load_in_hf: true
+    last_save_in_hf: true
+    interval: 500
+    async_mode: "disabled"
+  activation_checkpoint:
+    mode: full
+# Replay buffer configuration
+replay_buffer:
+  batch_size: ${local_batch_size}
+  max_policy_age: ${off_by_n}
+  # dp_size: ${trainer.parallelism.data_parallel_shard_degree} # Must equal trainer DP degree
+  dp_size: 32
+# Reference model configuration
+ref_model:
+  model:
+    name: qwen3
+    flavor: 32B
+    hf_assets_path: hf://${model}
+  training:
+    dtype: bfloat16
+    gc_freq: 1
+  compile:
+    enable: false
+  parallelism:
+    data_parallel_replicate_degree: 1
+    data_parallel_shard_degree: 1
+    tensor_parallel_degree: 4
+    pipeline_parallel_degree: 1
+    context_parallel_degree: 1
+    expert_parallel_degree: 1
+  checkpoint:
+    enable: true
+    initial_load_path: hf://${model}
+    initial_load_in_hf: true
+# All resource allocations
+services:
+  policy:
+    procs: ${policy.engine_args.tensor_parallel_size}
+    num_replicas: 1
+    hosts: 1
+    with_gpus: true
+    mesh_name: policy
+  ref_model:
+    procs: ${ref_model.parallelism.tensor_parallel_degree}
+    num_replicas: 1
+    with_gpus: true
+    mesh_name: ref_model
+  reward_actor:
+    procs: 1
+    num_replicas: 1
+    with_gpus: false
+    mesh_name: reward_actor
+actors:
+  dataset:
+    procs: 1
+    with_gpus: false
+    mesh_name: dataset
+  trainer:
+    procs: 8
+    hosts: 2
+    with_gpus: true
+    mesh_name: trainer
+  replay_buffer:
+    procs: 1
+    with_gpus: false
+    mesh_name: replay_buffer
+  compute_advantages:
+    procs: 1
+    with_gpus: false
+    mesh_name: compute_advantages
diff --git a/apps/grpo/qwen3_32b.yaml b/apps/grpo/qwen3_32b.yaml
index e7a0cf509..373d93be0 100644
--- a/apps/grpo/qwen3_32b.yaml
+++ b/apps/grpo/qwen3_32b.yaml
@@ -4,7 +4,7 @@
 
 # Global configuration
 group_size: 16
-local_batch_size: 32 # per-device batch size
+local_batch_size: 2 # per-device batch size
 max_req_tokens: 1024
 max_res_tokens: 1024
 model: "Qwen/Qwen3-32B"
@@ -14,7 +14,7 @@ provisioner:
   launcher: slurm
 
 # Main loop configuration
-rollout_threads: 32 # make this 4x the number of policy replicas seems to work well
+rollout_threads: 4 # make this 4x the number of policy replicas seems to work well
 
 # Observability configuration
 metric_logging:
@@ -69,8 +69,8 @@ trainer:
     enable: false
   parallelism:
     data_parallel_replicate_degree: 1
-    data_parallel_shard_degree: 1
-    tensor_parallel_degree: 8
+    data_parallel_shard_degree: -1
+    tensor_parallel_degree: 1
     pipeline_parallel_degree: 1
     context_parallel_degree: 1
     expert_parallel_degree: 1
@@ -90,7 +90,7 @@ replay_buffer:
   batch_size: ${local_batch_size}
   max_policy_age: ${off_by_n}
   # dp_size: ${trainer.parallelism.data_parallel_shard_degree} # Must equal trainer DP degree
-  dp_size: 1
+  dp_size: 64
 
 # Reference model configuration
 ref_model:
@@ -119,7 +119,7 @@ ref_model:
 services:
   policy:
     procs: ${policy.engine_args.tensor_parallel_size}
-    num_replicas: 4
+    num_replicas: 1
     hosts: 1
     with_gpus: true
     mesh_name: policy
@@ -141,7 +141,7 @@ actors:
     mesh_name: dataset
   trainer:
     procs: 8
-    hosts: 1
+    hosts: 4
     with_gpus: true
     mesh_name: trainer
   replay_buffer: