added the openreward support.

Author: shamanez

examples/agentic_demo/openreward_endless_terminals_IPA_qwen35_2b.yaml

@@ -51,7 +51,7 @@ skip_mock_system_prompt: true
 
 track_with: wandb
 tracker_kwargs:
-  api_key: "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"
+  api_key: ${oc.env:WANDB_API_KEY}
   project: roll-agentic
   name: ${exp_name}
 

examples/agentic_demo/openreward_endless_terminals_reinforce_qwen35_2b.yaml

@@ -0,0 +1,203 @@
+# OpenReward + EndlessTerminals + STEP_REINFORCE (vanilla PG) config
+# Simpler baseline without IPA chunk-level loss.
+# The model IS the agent — no iflow, no sandbox, no anti_call_llm.
+#
+# Usage:
+#   bash examples/agentic_demo/run_openreward_endless_terminals.sh reinforce
+#   # or directly:
+#   python examples/start_agentic_pipeline.py \
+#     --config_path agentic_demo \
+#     --config_name openreward_endless_terminals_reinforce_qwen35_2b
+
+defaults:
+  - ../config/deepspeed_zero@_here_
+  - ../config/deepspeed_zero2@_here_
+  - ../config/deepspeed_zero3@_here_
+  - ../config/deepspeed_zero3_cpuoffload@_here_
+
+hydra:
+  run:
+    dir: .
+  output_subdir: null
+
+exp_name: "openreward_endless_terminals_reinforce_qwen35_2b"
+seed: 42
+
+logging_dir: ./output/logs
+output_dir: ./output
+model_name: ${exp_name}-${now:%Y%m%d_%H%M%S}
+rollout_dump_dir: /home/ubuntu/ALE-latest/ROLL-personal/output/rollout_dump
+system_envs:
+  USE_MODELSCOPE: '1'
+
+checkpoint_config:
+  type: file_system
+  output_dir: /data
+
+num_gpus_per_node: 8
+rpc_timeout: 72000
+
+max_steps: 10
+save_steps: 50
+logging_steps: 1
+eval_steps: 0
+resume_from_checkpoint: false
+
+async_generation_ratio: 1
+parse_tool_call_parameter_to_dict: true
+skip_mock_system_prompt: true
+
+track_with: wandb
+tracker_kwargs:
+  api_key: ${oc.env:WANDB_API_KEY}
+  project: roll-agentic
+  name: ${exp_name}
+
+rollout_batch_size: 16
+val_batch_size: 1
+sequence_length: 32768
+
+max_tokens_per_step: 4096
+
+# --- Vanilla STEP_REINFORCE config ---
+advantage_clip: 0.2
+ppo_epochs: 1
+adv_estimator: "step_reinforce"
+batch_adjust_mode: "random_sample"
+step_reward_gamma: 1.0
+
+init_kl_coef: 0.0
+whiten_advantages: true
+entropy_loss_coef: 0
+max_grad_norm: 1.0
+
+# --- Model configs ---
+pretrain: /home/ubuntu/ALE-latest/model-checkpoints/Qwen3.5-2B
+reward_pretrain: /home/ubuntu/ALE-latest/model-checkpoints/Qwen3.5-2B
+actor_train:
+  model_args:
+    flash_attn: sdpa
+    attn_implementation: sdpa
+    disable_gradient_checkpointing: false
+    dtype: bf16
+    model_type: ~
+    freeze_module_prefix: vision_model
+  training_args:
+    learning_rate: 1.0e-6
+    weight_decay: 0
+    per_device_train_batch_size: 1
+    gradient_accumulation_steps: 4
+    warmup_steps: 0
+  data_args:
+    template: qwen3_coder
+  strategy_args:
+    strategy_name: megatron_train
+    strategy_config:
+      tensor_model_parallel_size: 2
+      pipeline_model_parallel_size: 1
+      expert_model_parallel_size: 1
+      context_parallel_size: 2
+      sequence_parallel: true
+      use_distributed_optimizer: true
+      recompute_granularity: full
+  device_mapping: list(range(0,4))
+  infer_batch_size: 1
+actor_infer:
+  model_args:
+    flash_attn: sdpa
+    attn_implementation: sdpa
+    disable_gradient_checkpointing: true
+    dtype: bf16
+  generating_args:
+    max_new_tokens: ${max_tokens_per_step}
+    top_p: 1.0
+    top_k: 50
+    num_beams: 1
+    temperature: 1.0
+    num_return_sequences: 1
+    stop_strings: ["</tool_call>"]
+    include_stop_str_in_output: true
+  data_args:
+    template: qwen3_coder
+  strategy_args:
+    strategy_name: vllm
+    strategy_config:
+      gpu_memory_utilization: 0.6
+      block_size: 16
+      load_format: auto
+      tensor_parallel_size: 1
+      max_model_len: 32768
+  device_mapping: list(range(0,8))
+
+reference:
+  model_args:
+    attn_implementation: sdpa
+    disable_gradient_checkpointing: true
+    dtype: bf16
+    model_type: ~
+    freeze_module_prefix: vision_model
+  data_args:
+    template: qwen3_coder
+  strategy_args:
+    strategy_name: megatron_infer
+    strategy_config:
+      tensor_model_parallel_size: 2
+      pipeline_model_parallel_size: 1
+      expert_model_parallel_size: 1
+      context_parallel_size: 2
+  device_mapping: list(range(0,4))
+  infer_batch_size: 1
+
+reward_normalization:
+  grouping: traj_group_id
+  method: identity
+
+# --- Environment config (OpenReward) ---
+max_actions_per_traj: 16
+env_manager_cls: roll.pipeline.agentic.env_manager.agent_native_env_manager.AgentNativeStepEnvManager
+
+train_env_manager:
+  max_env_num_per_worker: 1
+  num_env_groups: 1
+  group_size: 1
+  tags: [OpenRewardEndlessTerminalsTrain]
+  num_groups_partition: [1]
+
+val_env_manager:
+  max_env_num_per_worker: 1
+  num_env_groups: 1
+  group_size: 1
+  tags: [OpenRewardEndlessTerminalsVal]
+  num_groups_partition: [1]
+
+custom_envs:
+  OpenRewardEndlessTerminalsTrain:
+    env_type: "openreward_env"
+    max_steps: ${max_actions_per_traj}
+    max_tokens_per_step: ${max_tokens_per_step}
+    env_manager_cls: ${env_manager_cls}
+    agent_system_template: "unused — system prompt built dynamically from OpenReward tool specs"
+    agent_template: "unused — observation is full message list from OpenRewardEnv"
+    env_config:
+      environment_name: "kanishk/EndlessTerminals"
+      split: "train"
+      mode: "train"
+      max_steps: ${max_actions_per_traj}
+      reward_reduction: "sum"
+      nonterminal_reward: 0.0
+      retry_max_attempts: 3
+      retry_backoff_seconds: 5.0
+  OpenRewardEndlessTerminalsVal:
+    env_type: "openreward_env"
+    max_steps: ${max_actions_per_traj}
+    max_tokens_per_step: ${max_tokens_per_step}
+    env_manager_cls: ${env_manager_cls}
+    agent_system_template: "unused"
+    agent_template: "unused"
+    env_config:
+      environment_name: "kanishk/EndlessTerminals"
+      split: "train"
+      mode: "val"
+      max_steps: ${max_actions_per_traj}
+      reward_reduction: "sum"
+      nonterminal_reward: 0.0

examples/agentic_demo/run_openreward_endless_terminals.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+# Run OpenReward EndlessTerminals REINFORCE training with Qwen3.5-2B.
+#
+# Prerequisites:
+#   pip install openreward   # inside the docker container
+#
+# Usage (inside roll_openreward_runner container):
+#   export OPENREWARD_API_KEY="..."
+#   export WANDB_API_KEY="..."
+#   cd /home/ubuntu/ALE-latest/ROLL-personal
+#   bash examples/agentic_demo/run_openreward_endless_terminals.sh
+
+set -euo pipefail
+
+: "${OPENREWARD_API_KEY:?Set OPENREWARD_API_KEY}"
+: "${WANDB_API_KEY:?Set WANDB_API_KEY}"
+
+export NCCL_NET_PLUGIN=''
+export NCCL_TUNER_PLUGIN=''
+export NCCL_NET=Socket
+export PYTHONPATH="${PWD}:${PYTHONPATH:-}"
+
+python examples/start_agentic_pipeline.py \
+  --config_path agentic_demo \
+  --config_name openreward_endless_terminals_reinforce_qwen35_2b