Add files using upload-large-folder tool

.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

LICENSE

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README.md

@@ -0,0 +1,170 @@
+---
+language:
+- en
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-30B-A3B-Instruct-2507
+tags:
+- qwen3
+- qwen3-moe
+- reinforcement-learning
+- scientific-judgment
+- autonomous-science
+- materials-discovery
+- verified-environments
+- multi-turn-reasoning
+---
+
+# Dynamical-30B-A3B
+
+Dynamical-30B-A3B is a trained judgment layer for autonomous scientific workflows. It is a merged reinforcement-learning checkpoint built from `Qwen/Qwen3-30B-A3B-Instruct-2507` and trained in verified campaign environments that require sequential `select`, `validate`, and `revise` decisions under explicit budget constraints.
+
+The target capability is scientific judgment: selecting which candidate to investigate next, evaluating whether intermediate evidence is trustworthy or should be escalated to higher-fidelity verification, and revising beliefs about competing hypotheses as results accumulate. In this setting, the signal that determines whether a decision was correct lives in the process, not only in the final outcome.
+
+## Summary
+
+Autonomous scientific campaigns are stateful. A decision at round `t` changes which evidence becomes available at round `t+1`, and a wrong belief early in the campaign compounds through later decisions. Dynamical-30B-A3B was trained for this coupled loop rather than for isolated single-step reasoning.
+
+The model was trained in verified campaign environments built from:
+- a candidate source
+- a thermodynamic stability oracle
+- a staged verifier ladder with explicit budget costs
+
+In the materials-discovery instantiation used here, candidate crystal structures are generated with MatterGen, evaluated against the MP2020 convex hull to produce `E_hull`, and converted into staged evidence with increasing cost and fidelity.
+
+## Training Recipe
+
+The base model is `Qwen/Qwen3-30B-A3B-Instruct-2507`, a mixture-of-experts language model with 30B total parameters and 3B active parameters per token.
+
+Training proceeded in two stages:
+- SFT with LoRA rank 32 on 3,861 rollout rows from GPT-5.4 teacher demonstrations across 128 closed-world environments
+- Multi-turn RL with trajectory-level GRPO on 60 open-world environments
+
+The RL curriculum introduced harder budget tiers progressively:
+- steps 0-19: budget 9
+- steps 20-39: budgets 9 and 7
+- steps 40-100: budgets 5, 7, and 9
+
+The released model corresponds to the step-50 merged RL checkpoint.
+
+## What This Model Does
+
+Dynamical-30B-A3B is designed to operate at the decision point in an automated scientific workflow. It does not generate candidates, run experiments, or compute stability. It decides:
+- which candidate to prioritize
+- whether to trust, flag, or reject evidence
+- when to commit to or revise a hypothesis
+
+The model is therefore best understood as a judgment policy, not as a simulator or oracle.
+
+## Results
+
+The primary metric in the paper is hypothesis accuracy: the fraction of episodes where the model's highest-posterior hypothesis after all evidence rounds matches the oracle ground truth.
+
+On 15 held-out open-world environments with novel crystal structures:
+- Base: 46.7%
+- SFT: 53.3%
+- Dynamical-30B-A3B: 60.0%
+
+On 30 held-out closed-world environments:
+- Base: 32.2%
+- SFT: 40.0%
+- Dynamical-30B-A3B: 42.2%
+
+This closed-world result is reported as a retention check: open-world RL training does not produce regression on the training-adjacent domain.
+
+On MADE, an independent closed-loop materials-discovery benchmark:
+- formula recall: 0.156
+- structure recall: 0.085
+- stable efficiency: 0.233
+- AUDC: 0.279
+
+Relative to GPT-5.4 on MADE:
+- formula recall trails by 54%
+- structure recall exceeds by 67%
+
+The paper interprets this decomposition as evidence that judgment and knowledge are independently closable gaps: RL closes part of the judgment gap, while compositional exploration remains knowledge-limited.
+
+## Mechanistic Findings
+
+The main behavioral gain is not improved evidence discrimination. In the paper's signal-detection analysis, RL decreases `d'` from 0.770 to 0.471 while shifting the decision criterion from 1.606 to 0.801. The model becomes better calibrated about when to reject or escalate evidence; it does not become better at perceptually distinguishing admissible from inadmissible evidence.
+
+The paper also reports an emergent fast revision pathway. After RL, 11.8% of belief updates are "silent" low-uncertainty revisions, and these flip the leading hypothesis at 3.8x the rate of deliberate updates. This pathway is absent in the base model.
+
+## Intended Use
+
+This model is intended for research use in:
+- autonomous science agents
+- budget-constrained experimental planning
+- evidence escalation policies
+- multi-step scientific decision-making
+- studies of post-training for sequential judgment
+
+It is most appropriate when paired with an external system that already provides:
+- candidate generation
+- structured evidence
+- domain-specific verification oracles
+
+## Limitations
+
+The limitations reported in the paper should be treated as first-order caveats.
+
+- Checkpoint selection: step 50 was selected from 4 periodic evaluations on the same 15 held-out open-world environments, introducing mild optimistic bias.
+- Discrimination: RL teaches calibration, not discrimination. The model is better calibrated about when to reject, but not better at distinguishing admissible from inadmissible evidence.
+- Statistical power: the aggregate open-world evaluation uses 15 environments, and per-budget analyses use 5 environments per tier.
+- Curriculum: budget-5 environments received less training exposure than budget-9 environments.
+- Staged verifier: cheap and medium evidence are transforms of `E_hull`, not independent physical measurements.
+- Formula recall: the remaining gap in compositional exploration is not addressed by environment training alone.
+- External transfer: the SFT checkpoint did not complete MADE reliably, limiting SFT-vs-RL comparison there.
+
+This model should not be used as a substitute for physical simulation, laboratory verification, or expert review in high-stakes scientific settings.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Dynamical-Systems/Dynamical-30B-A3B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": "Given these candidates and staged measurements, which one should we validate next, and should we trust or escalate the current evidence?"
+    }
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=1024)
+
+print(tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True))
+```
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{barnes2026trainingscientificjudgment,
+  title={Training Scientific Judgment with Verified Environments for Autonomous Science},
+  author={Jarrod Barnes},
+  year={2026},
+  note={Technical report preprint}
+}
+```
+
+## Acknowledgments
+
+Dynamical-30B-A3B is derived from `Qwen/Qwen3-30B-A3B-Instruct-2507` and retains the upstream Apache 2.0 license.

chat_template.jinja

@@ -0,0 +1,61 @@
+{%- 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 %}
+{%- 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" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- 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 +1,38 @@
+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "decoder_sparse_step": 1,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 48,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_moe",
+  "moe_intermediate_size": 768,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000000,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

config_1m.json

@@ -0,0 +1,45 @@
+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "decoder_sparse_step": 1,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 48,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_moe",
+  "moe_intermediate_size": 768,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000000,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936,
+  "dual_chunk_attention_config":{
+    "chunk_size": 131072,
+    "local_size": 4096,
+    "original_max_position_embeddings": 131072,
+    "sparse_attention_enabled": true,
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+    "temperature": 0.7,
+    "top_k": 20,
+    "top_p": 0.8,
+    "transformers_version": "4.51.0"
+}

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