Add Gym GRPO example for multi-step tool calling with Workplace Assistant environment

examples/nemo_gym/grpo_workplace_assistant_nemotron_nano_v2_9b.yaml

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+grpo:
+  max_num_epochs: 1
+  num_prompts_per_step: 8
+  num_generations_per_prompt: 4
+  max_rollout_turns: 1 # for multi-turn rollouts. Workplace assistant has 1 turn but can have up to 6 tool-calling steps
+  max_num_steps: 72
+  normalize_rewards: true
+  use_leave_one_out_baseline: true
+  val_period: 6
+  val_at_start: true
+  overlong_filtering: false
+  max_val_samples: null   # inferred from size of val dataset. for multi evals, repeat val ds via `num_repeats` in `ng_prepare_data`.
+  val_batch_size: null
+  seed: 42
+  use_dynamic_sampling: false
+  dynamic_sampling_max_gen_batches: 10
+  batch_multiplier: 1
+  reward_shaping:
+    enabled: false
+    overlong_buffer_length: 128
+    overlong_buffer_penalty: 1
+    max_response_length: ${policy.max_total_sequence_length}
+  reward_scaling:
+    enabled: false
+    source_min: 0.0
+    source_max: 1.0
+    target_min: 0.0
+    target_max: 1.0
+  skip_reference_policy_logprobs_calculation: true
+
+loss_fn:
+  reference_policy_kl_penalty: 0
+  reference_policy_kl_type: "k3"
+  kl_input_clamp_value: 20.0
+  kl_output_clamp_value: 10.0
+  ratio_clip_min: 0.2
+  ratio_clip_max: 0.2
+  ratio_clip_c: null
+  # (default off) loss formulation improvements (docs/guides/grpo.md#loss)
+  use_on_policy_kl_approximation: false
+  truncated_importance_sampling_ratio: null
+  use_importance_sampling_correction: false
+  token_level_loss: true
+
+checkpointing:
+  enabled: true
+  checkpoint_dir: "results/grpo-workplace-assistant-nemotron-nano-v2-9b"
+  metric_name: "val:accuracy"
+  higher_is_better: true
+  keep_top_k: 3
+  save_period: 6  # Save checkpoint every 6 steps (aligned with val_period)
+  checkpoint_must_save_by: null
+
+policy:
+  model_name: "nvidia/NVIDIA-Nemotron-Nano-9B-v2"
+  tokenizer:
+    name: ${policy.model_name} ## specify if you'd like to use a tokenizer different from the model's default
+    chat_template_kwargs: null # can be used to pass kwargs to the chat template, e.g., enable_thinking=true
+  hf_config_overrides: {}
+  train_global_batch_size: ${mul:${grpo.num_prompts_per_step}, ${grpo.num_generations_per_prompt}}  # Match the total rollouts per step
+  train_micro_batch_size: 1
+  logprob_batch_size: 1
+  generation_batch_size: 32 # Only used when generating using HF backend
+  max_total_sequence_length: 28672
+  precision: "bfloat16"
+  logprob_chunk_size: null  # Disabled to allow defer_fp32_logits: false
+
+  dtensor_cfg:
+    _v2: false
+    enabled: false
+    cpu_offload: False
+    sequence_parallel: false
+    activation_checkpointing: true
+    tensor_parallel_size: 2
+    context_parallel_size: 1
+    custom_parallel_plan: null
+    clear_cache_every_n_steps: null
+
+  megatron_cfg:
+    enabled: true
+    bias_activation_fusion: false
+    tensor_model_parallel_size: 8
+    # We might want to consider setting this value higher (e.g. to 1) and raising the vllm generation max mem utilization
+    empty_unused_memory_level: 0
+    activation_checkpointing: true
+    # train_iters needs to be large enough to cover all training steps
+    train_iters: 100000
+    expert_tensor_parallel_size: 1
+    expert_model_parallel_size: 1
+    pipeline_model_parallel_size: 1
+    num_layers_in_first_pipeline_stage: null
+    num_layers_in_last_pipeline_stage: null
+    context_parallel_size: 1
+    pipeline_dtype: ${policy.precision}
+    sequence_parallel: false
+    freeze_moe_router: true
+    moe_router_dtype: "fp64"
+    moe_router_load_balancing_type: "none" # "seq_aux_loss" causes logprob error divergence for grpo
+    moe_router_bias_update_rate: 0.0 # by default, disable bias updates for grpo
+    #gives ~20% training perf speedup with sequence packing
+    apply_rope_fusion: True
+    defer_fp32_logits: false
+    moe_permute_fusion: false
+
+    optimizer:
+      optimizer: "adam"
+      lr: 5.0e-6
+      min_lr: 5.0e-7
+      weight_decay: 0.01
+      bf16: true
+      fp16: false
+      params_dtype: "float32"
+
+      #adam
+      adam_beta1: 0.9
+      adam_beta2: 0.999
+      adam_eps: 1e-8
+
+      #sgd
+      sgd_momentum: 0.9
+
+      #distributed optimizer
+      use_distributed_optimizer: true
+      use_precision_aware_optimizer: true
+
+      # optimizer cpu offload
+      optimizer_cpu_offload: false
+      optimizer_offload_fraction: 0.0
+
+      clip_grad: ${policy.max_grad_norm}
+
+    scheduler:
+      start_weight_decay: ${policy.megatron_cfg.optimizer.weight_decay}
+      end_weight_decay: ${policy.megatron_cfg.optimizer.weight_decay}
+      weight_decay_incr_style: "constant"
+      lr_decay_style: "constant"
+      lr_decay_iters: 100000  # Must be greater than lr_warmup_iters
+      lr_warmup_iters: 13
+      lr_warmup_init: 5.0e-7
+      # override_opt_param_scheduler: true
+
+    distributed_data_parallel_config:
+      grad_reduce_in_fp32: false
+      overlap_grad_reduce: true
+      overlap_param_gather: true
+      use_custom_fsdp: false
+      data_parallel_sharding_strategy: "optim_grads_params"
+
+    env_vars: null
+
+  # See docs/design-docs/sequence-packing-and-dynamic-batching.md 
+  # for more details on dynamic batching and sequence packing.
+  dynamic_batching:
+    enabled: False
+    train_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.train_micro_batch_size}}
+    logprob_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.logprob_batch_size}}
+    sequence_length_round: 64
+
+  sequence_packing:
+    enabled: false
+    train_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.train_micro_batch_size}}
+    logprob_mb_tokens: ${mul:${policy.max_total_sequence_length}, ${policy.logprob_batch_size}}
+    algorithm: "modified_first_fit_decreasing"
+    sequence_length_round: 64
+
+  # makes the training sequence length divisible by the tensor parallel size
+  # this is useful for sequence parallel training
+  make_sequence_length_divisible_by: 1
+  max_grad_norm: 1.0
+  offload_optimizer_for_logprob: false # Only useful for non-colocated generation since colocated generation will always offload optimizer to cuda before refit
+
+  optimizer: null
+
+  scheduler:
+    - name: "torch.optim.lr_scheduler.ConstantLR"
+      kwargs:
+        factor: 1.0
+        total_iters: 10000000000
+    - milestones: []
+
+  generation:
+    backend: "vllm"
+    max_new_tokens: ${policy.max_total_sequence_length}
+    temperature: 1.0
+    top_p: 1.0
+    top_k: null
+    stop_token_ids: null
+    stop_strings: null
+    vllm_cfg:
+      async_engine: true
+      precision: ${policy.precision}
+      # Must match megatron_cfg.tensor_model_parallel_size
+      tensor_parallel_size: 8
+      pipeline_parallel_size: 1
+      enable_expert_parallel: false
+      expert_parallel_size: 1
+      gpu_memory_utilization: 0.7
+      max_model_len: ${policy.max_total_sequence_length}
+      enforce_eager: false
+      use_deep_gemm: False
+      num_last_layers_in_bf16: 0
+      num_first_layers_in_bf16: 0
+      kv_cache_dtype: "auto"
+      expose_http_server: true
+      skip_tokenizer_init: false
+      http_server_serving_chat_kwargs:
+        # Workplace assistant uses 26 tools, so we enable auto_tools.
+        # For Nemotron Nano v2, we use the dedicated `nemotron_json` tool parser,
+        # registered via `nemotron_toolcall_parser_no_streaming.py`.
+        enable_auto_tools: true
+        tool_parser: nemotron_json
+    vllm_kwargs:
+      compilation_config:
+        # when enforce_eager is False, set ++policy.generation.vllm_kwargs.compilation_config.use_inductor=False for better accuracy,
+        # with the flag, vllm will use the custom CUDA kernels instead of the Triton kernels generated by torch.compile
+        # for more details, see convergence issue https://github.com/NVIDIA-NeMo/RL/issues/998
+        use_inductor: False
+        mamba_ssm_cache_dtype: "float32"
+    colocated:
+      # true: generation shares training GPUs
+      # false: uses dedicated generation resources
+      enabled: true
+      # only relevant when enabled is false
+      resources:
+        gpus_per_node: null # Decides num gpus to be dedicated to generation when there is one node in the cluster i.e cluster.num_nodes == 1
+        num_nodes: null # Decides number of nodes to be dedicated to generation
+
+data:
+  # Using the prepared train and validation datasets (downloaded from HuggingFace and split 90/10)
+  # Train: 1129 samples, Validation: 126 samples
+  train_jsonl_fpath: 3rdparty/Gym-workspace/Gym/resources_servers/workplace_assistant/data/train.jsonl
+  validation_jsonl_fpath: 3rdparty/Gym-workspace/Gym/resources_servers/workplace_assistant/data/validation.jsonl
+  agent_name: workplace_assistant_simple_agent
+  shuffle: true
+  num_workers: 0
+
+env:
+  should_use_nemo_gym: true
+  should_log_nemo_gym_responses: true  # If you have low logging storage, set this to false
+  nemo_gym:  # This is passed into NeMo-Gym as the initial_global_config_dict
+    config_paths:
+    - responses_api_models/vllm_model/configs/vllm_model_for_training.yaml  # Required! And it must be *for_training
+    - resources_servers/workplace_assistant/configs/workplace_assistant.yaml
+    workplace_assistant_simple_agent:
+      responses_api_agents:
+        simple_agent:
+          max_steps: 6  # Workplace assistant allows up to 6 tool-calling steps per task
+
+logger:
+  log_dir: "logs/grpo-workplace-assistant-nemotron-nano-v2-9b"  # Base directory for all logs
+  num_val_samples_to_print: 0 # Number of validation samples to pretty print on terminal
+  wandb_enabled: true
+  tensorboard_enabled: false
+  mlflow_enabled: false  # Disable MLflow logging
+  swanlab_enabled: false
+  monitor_gpus: true  # If true, will monitor GPU usage and log to wandb and/or tensorboard
+  wandb:
+    project: "grpo-workplace-assistant"
+    name: "nemotron-nano-v2-9b-workplace-assistant"
+  tensorboard: {}
+  mlflow:
+    experiment_name: "grpo-workplace-assistant"
+    run_name: "nemotron-nano-v2-9b-workplace-assistant"
+  gpu_monitoring:
+    collection_interval: 10  # How often to collect GPU usage metrics (in seconds)
+    flush_interval: 10  # How often to flush GPU usage metrics to the loggers (in seconds)
+
+cluster:
+  gpus_per_node: 8
+  num_nodes: 1  # Single node by default; set to 2+ for multi-node training