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Model: idealab-cs2/reappraisal-4b-grpo-rmv2
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-4B-Thinking-2507
+pipeline_tag: text-generation
+tags:
+- grpo
+- rlhf
+- reasoning
+- cognitive-reappraisal
+- emotion-regulation
+library_name: transformers
+---
+# Reappraisal-4B-GRPO-RMv2
+
+Cognitive reappraisal is reinterpreting a negative situation to reduce the emotion it causes.
+[Li, Herderich, Nair & Goldenberg (2025), *Skill but not Effort Drive GPT Overperformance over Humans in Cognitive Reframing of Negative Scenarios*](https://doi.org/10.31234/osf.io/fzvd8)
+collected reappraisals for 6 negative interpersonal scenarios from 611 people and from GPT-4-0314,
+and had human raters score each for effectiveness. This repo is part of a project that uses those
+ratings to train reward models of reappraisal effectiveness and to RL-finetune an open model that
+writes reappraisals as effective as possible — with GPT-4-0314, the AI reappraiser in the original
+study, as the bar to beat.
+
+This model is the strongest policy from that project: a GRPO finetune of
+[Qwen/Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507). Given a scenario,
+it produces an alternative interpretation addressed to the person, in two sentences or fewer, in a
+single forward pass (no best-of-N at inference). The reward signal is the three-seed ensemble
+reward model [idealab-cs2/reappraisal-reward-model-v2](https://huggingface.co/idealab-cs2/reappraisal-reward-model-v2).
+
+## Model Details
+
+- **Base Model:** Qwen/Qwen3-4B-Thinking-2507 (a reasoning model; non-reasoning bases lose here)
+- **Training Method:** GRPO (Group Relative Policy Optimization), full fine-tuning, DeepSpeed ZeRO-2, 4× H100
+- **Reward Model:** [idealab-cs2/reappraisal-reward-model-v2](https://huggingface.co/idealab-cs2/reappraisal-reward-model-v2) (3-seed ensemble, reward = `mean − 0.5·std`)
+- **Training Data:** scenarios from Li et al. (2025) — 6 negative interpersonal vignettes
+- **Trained By:** ruggsea
+
+## Evaluation
+
+Reappraisals were compared pairwise against GPT-4-0314's reappraisals for the same scenarios, judged
+by Llama-3.1-70B-Instruct scoring both A/B orderings. That judge was selected by calibration against
+the human preference data (it agrees with the human-preferred reappraisal 93% of the time on this
+task; larger judges such as Llama-3.1-405B-FP8 and GLM-5.2 agreed less and showed position bias).
+Results are pooled over three independent evals of this checkpoint (fresh generations + an
+independent both-orders judge each), and marginal wins are confirmed at n ≥ 240 with the lower CI
+bound above the bar.
+
+| model | win-rate vs GPT-4-0314 | n | 95% CI lower (iid, optimistic) |
+|---|---|---|---|
+| **Reappraisal-4B-GRPO-RMv2 (this model)** | **0.806** | 720 | **0.776** |
+| Reappraisal-4B-GRPO (v1, BT reward) | 0.581 | 480 | 0.537 |
+| Qwen3-4B-Thinking (base, untrained) | ~0.46 | — | — |
+| DeepSeek-R1-671B (single-pass reference) | 0.771 | 240 | 0.718 |
+
+**Read the CI column as a screening tool, not a precise bound.** The 720 contests cluster on only
+**6 vignettes** (n_effective ≈ 6), so the iid Wilson CIs above are optimistic, and the earlier claim
+that "the CI-low 0.776 sits above the R1 reference 0.771" is **retracted** — comparing an iid CI-low to
+a reference *point* is not a valid test. The recipe is seed-robust (a different-seed run scored 0.854),
+and the win over GPT-4-0314 on these 6 vignettes is real and clears the 0.608 bar with margin; but this
+is a **specialist trained on 6 scenarios**, and the strongest policy in this project is the
+[committee model](https://huggingface.co/idealab-cs2/reappraisal-4b-grpo-committee) (0.872 6-vignette,
+vignette-cluster CI-low 0.781), which is the checkpoint to read for the honest R1 comparison.
+
+**Out-of-distribution: this does NOT generalize past parity, and no 4B in this project beats R1
+off-distribution.** The policy is GRPO-trained on the same 6 vignettes it is evaluated on. The earlier
+"0.784 off-vignette, well above 0.5" claim is **retracted**: it used a weaker gemma opponent and iid
+statistics. Against a strong open 72B on fresh cross-family scenarios, our best policies are at
+**parity-or-below** (cluster CIs spanning 0.5), and against **DeepSeek-R1-671B off-distribution our 4B
+loses** — single-pass ~0.39–0.45 and best-of-N only tied on the selection pool before reverting to
+~0.375 on a fresh held-out pool (winner's curse). Off-distribution generalization tracks base capacity,
+not our training.
+
+The improvement over the v1 policy comes almost entirely from the reward model: the same GRPO recipe
+against the earlier reward models scored 0.581 (Bradley-Terry) and 0.698 (multidimensional), against
+0.806 with the v2 ensemble.
+
+## Prompting
+
+The model was trained with this system prompt:
+```
+You are helping someone reduce a negative emotion they feel in a short interpersonal scenario by
+offering an alternative interpretation of the situation (a 'reappraisal' / 'rethinking'). Direct
+your response at the person in the scenario in second person. Limit your response to two sentences
+maximum. Do not list emotions; output only the reappraisal text.
+```
+The user turn is `SCENARIO:\n{scenario}\n\nWrite a reappraisal of this scenario in two sentences
+maximum, addressing the person in second person.` Because the base is a thinking model, generations
+contain a reasoning trace in `<think>...</think>` followed by the reappraisal; only the text after
+`</think>` is the answer.
+
+## Training hyperparameters
+
+- learning_rate: 8e-6
+- beta (KL coefficient): 0.04 (kept strictly > 0; β=0 collapses and reward-hacks)
+- num_generations: 12
+- max_steps: 500
+- precision: bf16, DeepSpeed ZeRO-2 across 4 GPUs
+
+The reward is the ensemble score of the answer with a format gate penalizing outputs that are empty
+or longer than two sentences (so the policy cannot inflate reward through verbosity). Training was
+healthy: reward rising, KL bounded, entropy alive, no reward-std collapse (wandb `vzo3wpv6`).
+
+## Data separation
+
+The 6 vignettes' GPT-4-0314 reappraisals and the frontier-opponent answers are held out and used only
+for evaluation; training uses the vignette prompts (the task) and the human-rating-based reward model.
+Any pooled or synthetic training text is n-gram-checked against the held-out eval answers before use.
+
+## Intended use & limitations
+
+Research artifact for the study of RLHF, reward modeling, and computational emotion regulation. It
+writes short cognitive reappraisals of negative interpersonal situations. It is **not** a clinical or
+mental-health tool and must not be used as one. Quality is validated only on the narrow reappraisal
+task and the evaluation above.
+
+## References
+
+- Reward model used to train this policy: [idealab-cs2/reappraisal-reward-model-v2](https://huggingface.co/idealab-cs2/reappraisal-reward-model-v2)
+- Earlier policy (v1, Bradley-Terry reward): [idealab-cs2/reappraisal-4b-grpo](https://huggingface.co/idealab-cs2/reappraisal-4b-grpo)
+- Li, J. Z., Herderich, A., Nair, P., & Goldenberg, A. (2025). *Skill but not Effort Drive GPT Overperformance over Humans in Cognitive Reframing of Negative Scenarios.* PsyArXiv. https://doi.org/10.31234/osf.io/fzvd8

added_tokens.json

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+{
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+  "</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,
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+  "<|fim_suffix|>": 151661,
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+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
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+  "<|quad_start|>": 151650,
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+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- 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 %}
+{%- 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<think>\n' }}
+{%- endif %}

config.json

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+{
+  "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": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": [
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+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.6"
+}

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