Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

babyai_validation_layers.md

@@ -0,0 +1,162 @@
+# BABYAI Environment: Validation Layer Analysis
+
+## Overview
+
+BABYAI is one of the 8 validation environments. This document shows which validation scoring layers include BABYAI and how it contributes to the overall scoring system.
+
+## BABYAI Environment Details
+
+- **Environment Name**: `agentgym:babyai`
+- **Type**: AgentGym environment
+- **Dataset Size**: 500 tasks
+- **Daily Sampling Rate**: 120/day (fast environment)
+- **Task Type**: Grid-world navigation and instruction following
+- **Max Rounds**: 10
+- **Timeout**: 1200 seconds
+
+## Validation Layers That Include BABYAI
+
+BABYAI appears in validation layers 3-8 as part of various environment combinations. Here's the breakdown:
+
+### Layer 3 (3-environment combinations)
+
+**Total subsets with BABYAI**: C(7,2) = 21 combinations
+
+BABYAI appears in 21 out of 56 total Layer 3 subsets. Examples:
+- {BABYAI, WEBSHOP, ALFWORLD}
+- {BABYAI, SCIWORLD, TEXTCRAFT}
+- {BABYAI, SAT, DED}
+- {BABYAI, ABD, WEBSHOP}
+- ... (17 more combinations)
+
+**Weight per subset**: 8.0 / 56 = 0.143
+**Total potential weight from BABYAI Layer 3 subsets**: 21 × 0.143 = 3.003
+
+### Layer 4 (4-environment combinations)
+
+**Total subsets with BABYAI**: C(7,3) = 35 combinations
+
+BABYAI appears in 35 out of 70 total Layer 4 subsets. Examples:
+- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD}
+- {BABYAI, TEXTCRAFT, SAT, DED}
+- {BABYAI, ABD, WEBSHOP, ALFWORLD}
+- ... (32 more combinations)
+
+**Weight per subset**: 16.0 / 70 = 0.229
+**Total potential weight from BABYAI Layer 4 subsets**: 35 × 0.229 = 8.015
+
+### Layer 5 (5-environment combinations)
+
+**Total subsets with BABYAI**: C(7,4) = 35 combinations
+
+BABYAI appears in 35 out of 56 total Layer 5 subsets. Examples:
+- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, TEXTCRAFT}
+- {BABYAI, SAT, DED, ABD, WEBSHOP}
+- ... (33 more combinations)
+
+**Weight per subset**: 32.0 / 56 = 0.571
+**Total potential weight from BABYAI Layer 5 subsets**: 35 × 0.571 = 19.985
+
+### Layer 6 (6-environment combinations)
+
+**Total subsets with BABYAI**: C(7,5) = 21 combinations
+
+BABYAI appears in 21 out of 28 total Layer 6 subsets. Examples:
+- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, TEXTCRAFT, SAT}
+- {BABYAI, DED, ABD, WEBSHOP, ALFWORLD, SCIWORLD}
+- ... (19 more combinations)
+
+**Weight per subset**: 64.0 / 28 = 2.286
+**Total potential weight from BABYAI Layer 6 subsets**: 21 × 2.286 = 48.006
+
+### Layer 7 (7-environment combinations)
+
+**Total subsets with BABYAI**: C(7,6) = 7 combinations
+
+BABYAI appears in 7 out of 8 total Layer 7 subsets. Examples:
+- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, TEXTCRAFT, SAT, DED}
+- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, TEXTCRAFT, SAT, ABD}
+- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, TEXTCRAFT, DED, ABD}
+- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, SAT, DED, ABD}
+- {BABYAI, WEBSHOP, ALFWORLD, TEXTCRAFT, SAT, DED, ABD}
+- {BABYAI, WEBSHOP, SCIWORLD, TEXTCRAFT, SAT, DED, ABD}
+- {BABYAI, ALFWORLD, SCIWORLD, TEXTCRAFT, SAT, DED, ABD}
+
+**Weight per subset**: 128.0 / 8 = 16.0
+**Total potential weight from BABYAI Layer 7 subsets**: 7 × 16.0 = 112.0
+
+### Layer 8 (All 8 environments)
+
+**Total subsets with BABYAI**: C(7,7) = 1 combination
+
+BABYAI appears in the single Layer 8 subset:
+- {BABYAI, WEBSHOP, ALFWORLD, SCIWORLD, TEXTCRAFT, SAT, DED, ABD}
+
+**Weight per subset**: 256.0 / 1 = 256.0
+**Total potential weight from BABYAI Layer 8 subset**: 1 × 256.0 = 256.0
+
+## Summary Table
+
+| Layer | Total Subsets | Subsets with BABYAI | Weight per Subset | Total BABYAI Weight |
+|-------|---------------|---------------------|-------------------|-------------------|
+| 3     | 56            | 21                  | 0.143             | 3.003             |
+| 4     | 70            | 35                  | 0.229             | 8.015             |
+| 5     | 56            | 35                  | 0.571             | 19.985            |
+| 6     | 28            | 21                  | 2.286             | 48.006            |
+| 7     | 8             | 7                   | 16.0              | 112.0             |
+| 8     | 1             | 1                   | 256.0             | 256.0             |
+| **Total** | **219** | **120** | - | **447.009** |
+
+## Key Insights
+
+1. **BABYAI Coverage**: BABYAI appears in 120 out of 219 total evaluated subsets (54.8% of all subsets)
+
+2. **Exponential Importance**: As layers increase, BABYAI's potential contribution grows exponentially:
+   - Layer 3: 3.003 total weight
+   - Layer 8: 256.0 total weight (85× more!)
+
+3. **Comprehensive Performance Matters**: To maximize BABYAI-related rewards, a model must:
+   - Perform well on BABYAI alone (but this isn't evaluated in layers 1-2)
+   - Perform well on BABYAI + 2 other environments (Layer 3)
+   - Perform well on BABYAI + 3 other environments (Layer 4)
+   - ...
+   - Perform well on ALL 8 environments including BABYAI (Layer 8) - **highest reward!**
+
+4. **Layer 8 Dominance**: The single Layer 8 subset (all environments) contributes 256.0 weight, which is more than all other BABYAI-related subsets combined (191.009).
+
+## Relationship to 36 Transformer Layers
+
+The 36 transformer layers in the model architecture are **not directly mapped** to BABYAI. Instead:
+
+1. **All 36 layers work together** to process BABYAI tasks
+2. **BABYAI performance** is evaluated across all 8 environments
+3. **Validation scoring layers** (3-8) reward models that perform well on BABYAI in combination with other environments
+
+However, based on the codebase documentation (`BABYAI_SPECIFIC_IMPROVEMENTS.md`), there's evidence that:
+- **Late layers (24-35)** may be more important for BABYAI-specific improvements
+- BABYAI tasks (navigation/instruction-following) may benefit more from higher-level reasoning in later transformer layers
+
+## Scoring Example
+
+If a model performs well on BABYAI:
+
+**Scenario A**: Model excels on BABYAI + 2 other environments (wins 5 Layer 3 subsets)
+- Reward: 5 × 0.143 = 0.715
+
+**Scenario B**: Model excels on BABYAI + 6 other environments (wins 1 Layer 7 subset)
+- Reward: 1 × 16.0 = 16.0
+
+**Scenario C**: Model excels on ALL 8 environments including BABYAI (wins Layer 8)
+- Reward: 1 × 256.0 = 256.0
+
+**Result**: Comprehensive performance (Scenario C) gives 358× more reward than partial performance (Scenario A)!
+
+## Conclusion
+
+BABYAI is a critical component of the validation system, appearing in:
+- **120 out of 219 evaluated subsets** (54.8%)
+- **All validation layers 3-8**
+- **Maximum reward potential of 447.009** (if model wins all BABYAI-related subsets)
+
+To maximize BABYAI-related rewards, models must demonstrate comprehensive ability across multiple environments, with the highest reward (256.0) coming from performing well on all 8 environments simultaneously.
+

chat_template.jinja

@@ -0,0 +1,87 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{# o-5DaWwWm-inpdu-64402712 #}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,68 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 5000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.1",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.1"
+}

model-00001-of-00002.safetensors

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+size 4967215328

model-00002-of-00002.safetensors

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model.safetensors.index.json

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validation_scoring_example.md

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+# Concrete Example: Validation Scoring with 8 Environments
+
+## Scenario Setup
+
+Let's say we have 8 validation environments:
+1. WEBSHOP
+2. ALFWORLD  
+3. BABYAI
+4. SCIWORLD
+5. TEXTCRAFT
+6. SAT
+7. DED
+8. ABD
+
+And we're evaluating a model's performance across these environments.
+
+## Validation Layer Breakdown
+
+### Layer 3 (3-environment combinations)
+- **Total subsets**: C(8,3) = 56 combinations
+- **Examples**:
+  - {WEBSHOP, ALFWORLD, BABYAI}
+  - {SCIWORLD, TEXTCRAFT, SAT}
+  - {DED, ABD, WEBSHOP}
+  - ... (54 more combinations)
+- **Total weight for layer**: 2³ = 8.0
+- **Weight per subset**: 8.0 / 56 = **0.143**
+
+### Layer 4 (4-environment combinations)
+- **Total subsets**: C(8,4) = 70 combinations
+- **Examples**:
+  - {WEBSHOP, ALFWORLD, BABYAI, SCIWORLD}
+  - {TEXTCRAFT, SAT, DED, ABD}
+  - ... (68 more combinations)
+- **Total weight for layer**: 2⁴ = 16.0
+- **Weight per subset**: 16.0 / 70 = **0.229**
+
+### Layer 5 (5-environment combinations)
+- **Total subsets**: C(8,5) = 56 combinations
+- **Total weight for layer**: 2⁵ = 32.0
+- **Weight per subset**: 32.0 / 56 = **0.571**
+
+### Layer 6 (6-environment combinations)
+- **Total subsets**: C(8,6) = 28 combinations
+- **Total weight for layer**: 2⁶ = 64.0
+- **Weight per subset**: 64.0 / 28 = **2.286**
+
+### Layer 7 (7-environment combinations)
+- **Total subsets**: C(8,7) = 8 combinations
+- **Examples**:
+  - All environments except WEBSHOP
+  - All environments except ALFWORLD
+  - ... (6 more combinations)
+- **Total weight for layer**: 2⁷ = 128.0
+- **Weight per subset**: 128.0 / 8 = **16.0**
+
+### Layer 8 (All 8 environments)
+- **Total subsets**: C(8,8) = 1 combination
+- **The subset**: {WEBSHOP, ALFWORLD, BABYAI, SCIWORLD, TEXTCRAFT, SAT, DED, ABD}
+- **Total weight for layer**: 2⁸ = 256.0
+- **Weight per subset**: 256.0 / 1 = **256.0** (highest reward!)
+
+## Scoring Example
+
+Let's say Model A performs well on:
+- Layer 3: 10 out of 56 subsets (wins on 10 different 3-environment combinations)
+- Layer 4: 5 out of 70 subsets
+- Layer 5: 2 out of 56 subsets
+- Layer 6: 1 out of 28 subsets
+- Layer 7: 0 out of 8 subsets
+- Layer 8: 0 out of 1 subset (doesn't perform well on all 8 simultaneously)
+
+**Model A's total score** (simplified, assuming equal performance on winning subsets):
+```
+= (10 × 0.143) + (5 × 0.229) + (2 × 0.571) + (1 × 2.286) + (0 × 16.0) + (0 × 256.0)
+= 1.43 + 1.145 + 1.142 + 2.286 + 0 + 0
+= 6.003
+```
+
+Now let's say Model B performs well on:
+- Layer 3: 5 out of 56 subsets
+- Layer 4: 3 out of 70 subsets
+- Layer 5: 1 out of 56 subsets
+- Layer 6: 0 out of 28 subsets
+- Layer 7: 0 out of 8 subsets
+- Layer 8: **1 out of 1 subset** (performs well on ALL 8 environments!)
+
+**Model B's total score**:
+```
+= (5 × 0.143) + (3 × 0.229) + (1 × 0.571) + (0 × 2.286) + (0 × 16.0) + (1 × 256.0)
+= 0.715 + 0.687 + 0.571 + 0 + 0 + 256.0
+= 257.973
+```
+
+**Result**: Model B wins decisively because it performs well across all 8 environments simultaneously, earning the massive Layer 8 reward of 256.0!
+
+## Key Takeaways
+
+1. **Exponential Rewards**: Each layer gets 2× more total weight than the previous layer
+2. **Comprehensive Performance Matters**: Performing well on all 8 environments (Layer 8) gives 256× more weight than a single 3-environment combination
+3. **Distributed Weight**: Within each layer, weight is evenly distributed, so winning more subsets in a layer increases score
+4. **Top-Layer Focus**: Only layers 3-8 are evaluated, focusing on multi-environment capability
+
+## How This Relates to the 36 Transformer Layers
+
+The 36 transformer layers in the model work together to:
+1. Process input from any of the 8 environments
+2. Generate appropriate responses for each task type
+3. Learn representations that generalize across environments
+
+The validation scoring system then:
+1. Tests the model on all 8 environments
+2. Rewards models that perform well across multiple environments
+3. Uses combinatoric layers to incentivize comprehensive ability
+
+The 36 layers are the **capacity** (how the model processes information), while the 8 environments and combinatoric scoring are the **evaluation framework** (how we measure and reward performance).
+