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Model: esmaeil-abedi-dev/verifier-ce-qwen2.5-0.5b
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B
+pipeline_tag: text-generation
+tags:
+  - authorization
+  - ai-agent-safety
+  - verifier-as-reward
+  - rlvr
+---
+
+# verifier-ce-qwen2.5-0.5b
+
+A **Qwen2.5-0.5B** model fine-tuned to decide **AUTHORIZED / UNAUTHORIZED**
+for AI-agent actions over an object-capability delegation chain, using a
+**deterministic authorization verifier as the sole training signal**
+(verifier cross-entropy — the verdict as a target).
+
+Companion model to the paper *"Learnable Authorization: A Verifier-as-Reward
+Benchmark and Method for AI-Agent Authority."* Code, benchmark, and full
+experiment log: https://github.com/esmaeil-abedi-dev/verifier-as-reward
+
+## What it does
+
+Given a natural-language description of a root authority, a chain of
+delegations (each of which may only narrow the authority it received, with
+issue/expiry/revocation times), and a pending action, the model answers
+whether the action is authorized. The training target for every example is
+the verdict of the deterministic verifier `verify(...)` — never a
+hand-assigned or stored label.
+
+## Training
+
+- **Objective:** verifier cross-entropy, `-log π(verdict)` (see the paper /
+  repo for why sampled REINFORCE and exact expected-reward policy gradient
+  collapse or oscillate at this scale, and why the cross-entropy gradient's
+  non-saturation is what converges).
+- **Data:** synthetic execution traces across 5 domains and 9 scenario
+  classes, generated and labeled by the verifier; leakage-guarded train /
+  validation / test splits.
+- **Recipe:** class-balanced reward weighting, natural-language prompts,
+  full-parameter fine-tuning.
+
+## Evaluation
+
+Committed held-out test set (`benchmark_test.jsonl`, 80 actions,
+natural-language prompts, evaluated once). Three training seeds:
+
+| seed | accuracy | false-authorize (headline) | false-refuse |
+|---|---|---|---|
+| 7 | 0.988 | 0.023 | 0.000 |
+| 8 | 0.988 | 0.023 | 0.000 |
+| **9 (released weights)** | **0.975** | **0.045** | **0.000** |
+| mean | 0.983 ± 0.006 | 0.030 ± 0.011 | 0.000 |
+
+The **weights in this repository are the seed-9 checkpoint** (committed-test
+accuracy 0.975; seeds 7/8 reached 0.988). For calibration, on the identical
+prompts and metric: claude-sonnet-4.5 0.975, deepseek-r1 0.95,
+gemini-2.5-flash 0.875, a lexical heuristic floor 0.80, llama-3.3-70b 0.775,
+untrained baseline ~0.47. The only imperfect class is `chain_structure`
+(0.90) — the hardest structural-reasoning class.
+
+**These are in-distribution results** (train/validation/test drawn from one
+generator, deduplicated at the decision-context level). They do not by
+themselves establish out-of-distribution generalization; see the
+repository's domain-hold-out experiment.
+
+## Intended use and limitations
+
+- **Research artifact**, not a production authorization system. The verifier
+  itself is the ground truth; this model approximates it.
+- Trained and evaluated on a **synthetic** benchmark from a single verifier;
+  generalization to real agent logs or to authorization models outside the
+  attenuated-delegation formalism is not established here.
+- See the repository's `EXPERIMENT_LOG.md` for exact metrics (including the
+  in-distribution vs. out-of-distribution domain-hold-out results) and their
+  confidence intervals, and `DATASHEET.md` for the benchmark's limitations.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tok = AutoTokenizer.from_pretrained("esmaeil-abedi-dev/verifier-ce-qwen2.5-0.5b")
+model = AutoModelForCausalLM.from_pretrained("esmaeil-abedi-dev/verifier-ce-qwen2.5-0.5b")
+```
+
+Or evaluate it on any benchmark split with the repo's harness (no retraining):
+
+```bash
+PYTHONPATH=. python train_verifier_reward.py \
+    --eval-checkpoint esmaeil-abedi-dev/verifier-ce-qwen2.5-0.5b \
+    --test-file benchmark_test.jsonl
+```