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wandb/run-20260601_231055-jqxjy52g/files/media/table/rollout/reward_table_355_0a23e34d64ac058d9003.table.json

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wandb/run-20260601_231055-jqxjy52g/files/media/table/rollout/reward_table_356_5ae1f5c01d1c88eb7cb1.table.json

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wandb/run-20260601_231055-jqxjy52g/files/media/table/rollout/reward_table_55_8e4b71318abf2634a9cc.table.json

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wandb/run-20260601_231055-jqxjy52g/files/media/table/rollout/reward_table_56_744d049a11288a7662d6.table.json

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wandb/run-20260602_110242-uv8l2q7y/files/config.yaml

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+            use_torch_compile: true
+        rollout:
+            _target_: verl.workers.config.RolloutConfig
+            agent:
+                _target_: verl.workers.config.AgentLoopConfig
+                agent_loop_config_path: null
+                custom_async_server:
+                    _target_: verl.workers.config.CustomAsyncServerConfig
+                    name: null
+                    path: null
+                num_workers: 8
+            calculate_log_probs: false
+            cudagraph_capture_sizes: null
+            data_parallel_size: 1
+            disable_log_stats: true
+            do_sample: true
+            dtype: bfloat16
+            enable_chunked_prefill: true
+            enable_prefix_caching: true
+            enforce_eager: true
+            expert_parallel_size: 1
+            free_cache_engine: true
+            gpu_memory_utilization: 0.7
+            gradient_analysis_bucket_mode: quantile
+            gradient_analysis_num_buckets: 6
+            ignore_eos: false
+            layered_summon: false
+            load_format: auto
+            log_prob_max_token_len_per_gpu: 16384
+            log_prob_micro_batch_size: null
+            log_prob_micro_batch_size_per_gpu: 1
+            log_prob_use_dynamic_bsz: false
+            max_model_len: 16384
+            max_num_batched_tokens: 16384
+            max_num_seqs: 1024
+            mode: sync
+            multi_stage_wake_up: false
+            multi_turn:
+                _target_: verl.workers.config.MultiTurnConfig
+                enable: false
+                format: hermes
+                interaction_config_path: null
+                max_assistant_turns: null
+                max_parallel_calls: 1
+                max_tool_response_length: 256
+                max_user_turns: null
+                num_repeat_rollouts: null
+                tokenization_sanity_check_mode: strict
+                tool_config_path: null
+                tool_response_truncate_side: middle
+                use_inference_chat_template: false
+            "n": 1
+            name: vllm
+            over_sample_rate: 0
+            profiler:
+                _target_: verl.utils.profiler.ProfilerConfig
+                all_ranks: false
+                enable: false
+                ranks: []
+                save_path: outputs/profile
+                tool: null
+                tool_config:
+                    npu:
+                        _target_: verl.utils.profiler.config.NPUToolConfig
+                        analysis: true
+                        contents: []
+                        discrete: false
+                        level: level1
+                    nsys:
+                        _target_: verl.utils.profiler.config.NsightToolConfig
+                        discrete: false
+                    torch:
+                        _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+                        step_end: null
+                        step_start: 0
+                    torch_memory:
+                        _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+                        stack_depth: 32
+                        trace_alloc_max_entries: 100000
+            prompt_length: 1
+            response_length: 400
+            rollout_filter_empty_stop_steps: 5
+            rollout_filter_include_zero: true
+            rollout_filter_metric: reward_variance
+            rollout_filter_selection_eps: 0.01
+            rollout_filter_strategy: top_p
+            rollout_filter_top_p_prob_mode: linear
+            rollout_filter_type: largest
+            rollout_filter_value: 0.9
+            skip_dump_dir: /tmp/rollout_dump
+            skip_rollout: false
+            skip_tokenizer_init: true
+            temperature: 1
+            tensor_model_parallel_size: 1
+            top_k: -1
+            top_p: 1
+            trace:
+                _target_: verl.workers.config.TraceConfig
+                backend: null
+                token2text: false
+            update_weights_bucket_megabytes: 512
+            val_kwargs:
+                _target_: verl.workers.config.SamplingConfig
+                do_sample: true
+                "n": 1
+                temperature: 0.5
+                top_k: -1
+                top_p: 1
+agent_proxy:
+    value:
+        action_sep: '||'
+        batch_adjust_mode: copy
+        context_window_mode: full
+        enable_think: true
+        max_actions_per_turn: 3
+        max_context_window: -1
+        max_turn: 20
+        reward_normalization:
+            grouping: state
+            method: identity
+        use_turn_scores: false
+algorithm:
+    value:
+        _target_: verl.trainer.config.AlgoConfig
+        adv_estimator: gae
+        bi_level_gae: false
+        gamma: 1
+        high_level_gamma: 0.95
+        kl_ctrl:
+            _target_: verl.trainer.config.KLControlConfig
+            horizon: 10000
+            kl_coef: 0
+            target_kl: 0.1
+            type: fixed
+        kl_penalty: kl
+        lam: 1
+        norm_adv_by_std_in_grpo: true
+        pf_ppo:
+            reweight_method: pow
+            weight_pow: 2
+        soft_advantage_reweight: false
+        use_kl_in_reward: false
+        use_pf_ppo: false
+        zero_task_advantage: false
+collapse_detection:
+    value:
+        compute_freq: 5
+        first_turn_enabled: true
+        micro_batch_size: 128
+        multi_turn_enabled: true
+        num_samples: 64
+critic:
+    value:
+        _target_: verl.workers.config.FSDPCriticConfig
+        checkpoint:
+            _target_: verl.trainer.config.CheckpointConfig
+            async_save: false
+            load_contents:
+                - model
+                - optimizer
+                - extra
+            save_contents:
+                - model
+                - optimizer
+                - extra
+        cliprange_value: 0.5
+        enable: null
+        forward_max_token_len_per_gpu: 32768
+        forward_micro_batch_size: null
+        forward_micro_batch_size_per_gpu: 1
+        grad_clip: 1
+        loss_agg_mode: token-mean
+        model:
+            _target_: verl.workers.config.FSDPCriticModelCfg
+            enable_activation_offload: false
+            enable_gradient_checkpointing: true
+            external_lib: null
+            fsdp_config:
+                _target_: verl.workers.config.FSDPEngineConfig
+                entropy_checkpointing: false
+                entropy_from_logits_with_chunking: false
+                forward_only: false
+                forward_prefetch: false
+                fsdp_size: -1
+                model_dtype: fp32
+                offload_policy: false
+                optimizer_offload: false
+                param_offload: false
+                reshard_after_forward: true
+                strategy: fsdp
+                ulysses_sequence_parallel_size: 1
+                use_orig_params: false
+                use_torch_compile: true
+                wrap_policy:
+                    min_num_params: 0
+            lora_alpha: 64
+            lora_rank: 0
+            path: /mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+            target_modules: all-linear
+            tokenizer_path: /mnt/general/share/model/Qwen/Qwen2.5-3B-Instruct
+            trust_remote_code: false
+            use_remove_padding: false
+            use_shm: false
+        optim:
+            _target_: verl.workers.config.FSDPOptimizerConfig
+            betas:
+                - 0.9
+                - 0.999
+            clip_grad: 1
+            lr: 1e-05
+            lr_warmup_steps: -1
+            lr_warmup_steps_ratio: 0
+            min_lr_ratio: 0
+            num_cycles: 0.5
+            total_training_steps: 1000
+            warmup_style: constant
+            weight_decay: 0.01
+        ppo_epochs: 1
+        ppo_max_token_len_per_gpu: 32768
+        ppo_micro_batch_size: null
+        ppo_micro_batch_size_per_gpu: 1
+        ppo_mini_batch_size: 32
+        profiler:
+            _target_: verl.utils.profiler.ProfilerConfig
+            all_ranks: false
+            enable: false
+            ranks: []
+            save_path: outputs/profile
+            tool: null
+            tool_config:
+                npu:
+                    _target_: verl.utils.profiler.config.NPUToolConfig
+                    analysis: true
+                    contents: []
+                    discrete: false
+                    level: level1
+                nsys:
+                    _target_: verl.utils.profiler.config.NsightToolConfig
+                    discrete: false
+                torch:
+                    _target_: verl.utils.profiler.config.TorchProfilerToolConfig
+                    step_end: null
+                    step_start: 0
+                torch_memory:
+                    _target_: verl.utils.profiler.config.TorchMemoryToolConfig
+                    stack_depth: 32
+                    trace_alloc_max_entries: 100000
+        rollout_n: 1
+        shuffle: false
+        strategy: fsdp
+        ulysses_sequence_parallel_size: 1
+        use_dynamic_bsz: false
+ctx_manager:
+    value:
+        generation:
+            gen_config:
+                kwargs: null
+                response_length: 400
+                temperature: 1
+                top_k: -1
+                top_p: 1
+custom_envs:
+    value:
+        Alfworld:
+            env_config:
+                eval_dataset: eval_in_distribution
+            env_instruction: |
+                You are an expert agent in the ALFRED Embodied Environment.
+                Complete household tasks by navigating and interacting with objects.
+
+                You should first reason step-by-step about the current situation. This reasoning process MUST be enclosed within <think> </think> tags.
+                Once you've finished your reasoning, you should choose an admissible action for current step and present it within <answer>...</answer> tags.
+            env_type: alfworld
+            max_actions_per_traj: 50
+            max_tokens: 512
+            max_workers: 1
+            parallel_friendly: false
+        AlfworldOOD:
+            env_config:
+                eval_dataset: eval_out_of_distribution
+            env_instruction: |
+                You are an expert agent in the ALFRED Embodied Environment.
+                Complete household tasks by navigating and interacting with objects.
+
+                You should first reason step-by-step about the current situation. This reasoning process MUST be enclosed within <think> </think> tags.
+                Once you've finished your reasoning, you should choose an admissible action for current step and present it within <answer>...</answer> tags.
+            env_type: alfworld
+            max_actions_per_traj: 50
+            max_tokens: 512
+            max_workers: 1
+            parallel_friendly: false
+        Bandit:
+            env_config:
+                split: train
+            env_instruction: ""
+            env_type: bandit
+            max_actions_per_traj: 1
+            max_tokens: 100
+            max_workers: 32
+            parallel_friendly: false
+        BanditTest:
+            env_config:
+                split: test
+            env_instruction: ""
+            env_type: bandit
+            max_actions_per_traj: 1
+            max_tokens: 100
+            max_workers: 32
+            parallel_friendly: false
+        CoordFrozenLake:
+            env_config:
+                observation_format: grid_coord
+                success_rate: 0.8
+            env_instruction: |
+                You are solving the FrozenLake puzzle. The observation includes both a symbol grid and zero-indexed coordinates for the start, goal, player, and any holes.
+                Coordinates range from the top-left corner (0, 0) to the bottom-right corner (5, 5).
+                Beware that the ice is slippery, so the agent might slide and end up in an unintended tile.
+                Respond with a sequence of actions such as <answer>Left || Up || Up</answer>.
+            env_type: frozen_lake
+            max_actions_per_traj: 10
+            max_tokens: 120
+            max_workers: 32
+            parallel_friendly: false
+        CoordSokoban:
+            env_config:
+                dim_x: 6
+                dim_y: 6
+                max_steps: 100
+                num_boxes: 1
+                observation_format: grid_coord
+            env_instruction: "You are solving the Sokoban puzzle. You are the player and you need to push all boxes to targets.\nYou are provided with a symbol grid and the zero-indexed coordinates of the player, each box, and each target. \nCoordinates range from the top-left corner (0, 0) to the bottom-right corner (5, 5). \nWhen you are exactly next to a box, you can push it by moving in the same direction. \nYou cannot push a box through a wall, and you cannot pull a box.\nThe answer should be a sequence of actions, like <answer>Right || Right || Up</answer>.\n"
+            env_type: sokoban
+            max_actions_per_traj: 10
+            max_tokens: 120
+            max_workers: 32
+            parallel_friendly: false
+        Countdown:
+            env_config: null
+            env_instruction: 'You are solving the Countdown puzzle. You should use the num list to create an equation that equals the target. Example answer format: <think> To find an equation using [3, 5, 2] to get 4. Let''s check 2 + 5 = 7, 7 - 3 = 4. So the answer is 2 + 5 - 3 = 4. </think><answer>2 + 5 - 3</answer>'
+            env_type: countdown
+            max_actions_per_traj: 1
+            max_tokens: 100
+            max_workers: 32
+            parallel_friendly: false
+        DeepCoder:
+            env_config:
+                max_steps: 1
+            env_instruction: 'You are solving a coding task. Provide a complete Python function solution only. Format: <answer>...</answer>'
+            env_type: deepcoder
+            max_actions_per_traj: 1
+            max_tokens: 8000
+            max_workers: 32
+            parallel_friendly: true
+        FrozenLake:
+            env_config:
+                success_rate: 0.8
+            env_instruction: 'You are solving the FrozenLake puzzle. Forbid the whole and go to the target. You may move to the unintended direction due to the slippery ice. Example answer format: <think>To forbid the hole and go to the target, I should go left then go up.</think><answer>Left || Up</answer>'
+            env_type: frozen_lake
+            max_actions_per_traj: 10
+            max_tokens: 100
+            max_workers: 32
+            parallel_friendly: false
+        LargerSokoban:
+            env_config:
+                dim_x: 8
+                dim_y: 8
+                max_steps: 100
+                num_boxes: 2
+                search_depth: 10
+            env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and you need to push all boxes to targets. \nWhen you are right next to a box, you can push it by moving in the same direction. \nYou cannot push a box through a wall, and you cannot pull a box. \nThe answer should be a sequence of actions, like <answer>Right || Right || Up</answer>\n"
+            env_type: sokoban
+            max_actions_per_traj: 10
+            max_tokens: 100
+            max_workers: 32
+            parallel_friendly: false
+        Lean:
+            env_config: null
+            env_instruction: You are a Lean theorem prover. Given a Lean theorem statement, propose a sequence of tactics that completes the proof. Think step by step about which tactics to apply next. Provide tactics separated by '||', for example <answer>intro || simp || rfl</answer>.
+            env_type: lean
+            max_actions_per_traj: 30
+            max_tokens: 512
+            max_workers: 32
+            parallel_friendly: true
+        MediumSudoku:
+            env_config:
+                difficulty: medium
+                grid_size: 9
+                max_steps: 81
+                render_format: with_feedback
+                show_conflicts: true
+                show_valid_numbers: true
+            env_instruction: |
+                You are solving a Sudoku puzzle. Fill in the grid so that every row, column, and 3x3 box contains the numbers 1-9 without repetition.
+                Initial cells are shown in [brackets] and cannot be modified. Empty cells are shown as dots (.).
+                Place numbers one at a time using the format: <answer>place 5 at row 2 col 3</answer> or <answer>2,3,5</answer>
+                The environment will provide feedback on valid/invalid moves and show conflicts if any occur.
+            env_type: sudoku
+            max_actions_per_traj: 30
+            max_tokens: 150
+            max_workers: 32
+            parallel_friendly: false
+        MetamathQA:
+            env_config: null
+            env_instruction: 'You are solving Math problems. '
+            env_type: metamathqa
+            max_actions_per_traj: 1
+            max_tokens: 100
+        SearchQA:
+            env_config:
+                max_search_results: 5
+                max_steps: 10
+            env_instruction: |
+                You are a search agent answering questions by searching for information.
+                Use search[your query] to find relevant documents, and finish[your answer] to submit your final answer.
+
+                You should first reason step-by-step about the current situation. This reasoning process MUST be enclosed within <think> </think> tags.
+                Then provide your action within <answer>...</answer> tags.
+
+                Examples:
+                  <think>I need to find information about Ben Platt's father.</think><answer>search[Ben Platt father parent]</answer>
+                  <think>Based on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry Platt]</answer>
+            env_type: search
+            max_actions_per_traj: 10
+            max_tokens: 300
+            max_workers: 32
+            parallel_friendly: true
+        SearchQAMock:
+            env_config:
+                max_search_results: 5
+                max_steps: 10
+                mock_mode: true
+            env_instruction: |
+                You are a search agent answering questions by searching for information.
+                Use search[your query] to find relevant documents, and finish[your answer] to submit your final answer.
+
+                You should first reason step-by-step about the current situation. This reasoning process MUST be enclosed within <think> </think> tags.
+                Then provide your action within <answer>...</answer> tags.
+
+                Examples:
+                  <think>I need to find information about Ben Platt's father.</think><answer>search[Ben Platt father parent]</answer>
+                  <think>Based on the search results, Ben Platt's father is Henry Platt.</think><answer>finish[Henry Platt]</answer>
+            env_type: search
+            max_actions_per_traj: 10
+            max_tokens: 300
+            max_workers: 32
+            parallel_friendly: true
+        SimpleSokoban:
+            env_config:
+                dim_x: 6
+                dim_y: 6
+                max_steps: 100
+                num_boxes: 2
+            env_instruction: "You are a careful Sokoban solver. Use these reusable abstractions when reasoning:\n1. First compare the box position with the target position; the useful push directions are usually the directions that reduce their row/column distance.\n2. Before pushing, move the player to the square opposite the intended push direction. A move that only repositions the player can be useful if it sets up the next push.\n3. Never push a box into a wall, corner, or narrow dead end unless that square is the target or clearly on the only path to the target.\n4. Prefer short plans that move the single box steadily toward the target; avoid wandering moves that do not improve player position or box position.\n5. If the box and target are aligned in the same row or column, try to keep the box on that line and push along it.\n6. If they are not aligned, first push to fix one coordinate, then reposition and push to fix the other coordinate.\n7. Check that after each push, the player can still reach the next required pushing side of the box.\n8. Output only the required XML-like format and choose valid Sokoban actions.\n\nYou are the player and you need to push all boxes to targets. \nWhen you are right next to a box, you can push it by moving in the same direction. \nYou cannot push a box through a wall, and you cannot pull a box. \nThe answer should be a sequence of actions, like <answer>Right || Right || Up</answer>\n"
+            env_type: sokoban
+            max_actions_per_traj: 20
+            max_tokens: 100
+            max_workers: 32
+            parallel_friendly: false
+        SimpleSudoku:
+            env_config:
+                difficulty: easy
+                grid_size: 4
+                max_steps: 20
+                render_format: with_feedback
+                show_conflicts: false
+                show_valid_numbers: false
+            env_instruction: |
+                You are solving a Sudoku puzzle. Fill in the grid so that every row, column, and 2x2 box contains the numbers 1-4 without repetition.
+                Initial cells are shown in [brackets] and cannot be modified. Empty cells are shown as dots (.).
+                Place numbers one at a time using the format, for example: <answer>place 1 at row 2 col 3</answer> or <answer>1,2,3</answer>
+                The environment will provide feedback on valid/invalid moves and show conflicts if any occur.
+            env_type: sudoku
+            max_actions_per_traj: 20
+            max_tokens: 150
+            max_workers: 32
+            parallel_friendly: false
+        SokobanDifferentGridVocab:
+            env_config:
+                dim_x: 6
+                dim_y: 6
+                grid_lookup:
+                    "0": W
+                    "1": .
+                    "2": G
+                    "3": C
+                    "4": B
+                    "5": A
+                    "6": '@'
+                grid_vocab:
+                    .: empty
+                    '@': player on target
+                    A: player
+                    B: box
+                    C: box on target
+                    G: target
+                    W: wall
+                max_steps: 100
+                num_boxes: 1
+                search_depth: 30
+            env_instruction: "You are solving the Sokoban puzzle. \nYou are the player and you need to push all boxes to targets. \nWhen you are right next to a box, you can push it by moving in the same direction. \nYou cannot push a box through a wall, and you cannot pull a box. \nThe answer should be a sequence of actions, like <answer>Right || Right || Up</answer>\n"
+            env_type: sokoban
+            max_actions_per_traj: 10
+            max_tokens: 100
+            max_workers: 32
+            parallel_friendly: false
+        VisualSimpleSokoban:
+            env_config:
+                dim_x: 6
+                dim_y: 6
+                max_steps: 100
+                num_boxes: 1
+                render_mode: rgb_array
+            env_instruction: You are solving the Sokoban puzzle. You are the player and you need to push all boxes to targets. When you are right next to a box, you can push it by moving in the same direction. You cannot push a box through a wall, and you cannot pull a box. The answer should be a sequence of actions, like <answer>Right || Right || Up</answer>
+            env_type: sokoban
+            max_actions_per_traj: 10
+            max_tokens: 100
+            max_workers: 32
+            parallel_friendly: false
+        WebShop:
+            env_config:
+                dataset: small
+            env_instruction: 'You are browsing an online shop. Based on the instruction, buy a product that close to the production description. You need to search, read the search results, pick a product, choose the size and color and buy. You should only choose action from the available actions list provided later.  Example process: I need a gingko light and 20x20 pillow cover that is hand painted. First search[gingko light 20x20 pillow cover hand painted], answer format: <answer>search[blanket with fleece throw]</answer>. Valid answer is search[<keywords>] or click[<clickable>].'
+            env_type: webshop
+            max_actions_per_traj: 9
+            max_tokens: 200
+        WebShopFull:
+            env_config:
+                dataset: full
+            env_instruction: You are an expert autonomous agent operating in the WebShop e‑commerce environment.
+            env_type: webshop
+            max_actions_per_traj: 15
+            max_tokens: 200
+        game_2048:
+            env_config: null
+            env_instruction: |
+                You are playing the 2048 game on a 4x4 grid. Merge equal tiles by sliding Up, Right, Down, or Left.
+                If a move is invalid (no tiles move), a small penalty is applied. Respond with a single action.
+                Example: <answer>Up</answer>
+            env_type: game_2048
+            max_actions_per_traj: 700
+            max_tokens: 8192
+        rubikscube:
+            env_config:
+                max_steps: 20
+                render_mode: text
+                scramble_depth: 1
+            env_instruction: |
+                You are solving a 2x2 Rubik's Cube (Pocket Cube). The goal is to restore the cube so that each of the faces consists of a single, unique color.
+                Available actions use standard Singmaster notation for face rotations: U, U', D, D', L, L', R, R', F, F', B, B'.
+                - Faces: U (Up), D (Down), L (Left), R (Right), F (Front), B (Back).
+                - Modifiers: A letter alone means 90° clockwise (e.g., 'R'). A letter with prime (') means 90° counter-clockwise (e.g., "R'").
+                Respond with a sequence of actions separated by "||".
+                Example: <answer>U</answer>
+            env_type: rubikscube
+            max_actions_per_traj: 20
+            max_tokens: 96
+custom_reward_function:
+    value:
+        name: compute_score
+        path: null
+data:
+    value:
+        custom_cls:
+            name: null
+            path: null
+        datagen:
+            name: null
+            path: null
+        dataloader_num_workers: 8
+        filter_overlong_prompts: false
+        filter_overlong_prompts_workers: 1
+        image_key: images
+        max_prompt_length: null
+        max_response_length: null
+        prompt_key: prompt
+        return_full_prompt: false
+        return_multi_modal_inputs: true
+        return_raw_chat: false
+        return_raw_input_ids: false
+        reward_fn_key: data_source
+        sampler:
+            class_name: null
+            class_path: null
+        shuffle: true
+        tokenizer: null
+        train_batch_size: 128
+        train_files: ~/data/rlhf/gsm8k/train.parquet
+        truncation: error
+        trust_remote_code: false
+        use_shm: false
+        val_batch_size: null
+        val_files: ~/data/rlhf/gsm8k/test.parquet
+        validation_shuffle: false
+        video_key: videos
+enable_response_mask:
+    value: true
+es_manager:
+    value:
+        format_penalty: -0.1
+        train:
+            env_configs:
+                n_groups:
+                    - 8
+                tags:
+                    - SimpleSudoku
+            env_groups: 8
+            group_size: 16
+        val:
+            env_configs:
+                n_groups:
+                    - 32
+                tags:
+                    - SimpleSudoku
+            env_groups: 32
+            group_size: 16
+global_profiler:
+    value:
+        _target_: verl.utils.profiler.ProfilerConfig
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wandb/run-20260602_110242-uv8l2q7y/files/requirements.txt

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wandb/run-20260602_110242-uv8l2q7y/files/wandb-metadata.json

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wandb/run-20260602_110242-uv8l2q7y/files/wandb-summary.json

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wandb/run-20260602_110620-4iub9iga/files/media/table/rollout/reward_table_10_e589d01346a3e92b5e08.table.json

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wandb/run-20260602_110620-4iub9iga/files/media/table/rollout/reward_table_12_c851e821280d2b36431c.table.json

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