ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter8/AdaptThink/TRAINING_REPORT.md

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+# Experiment 8-10: AdaptThink training report
+
+This report records the historical AdaptThink 1.5B, δ=0.05 training run used by
+the book. It is a training report, not a fresh local reproduction. In accordance
+with the book's distribution policy, model checkpoints are not distributed.
+
+## Public runs
+
+- Main training run: [`wubbn5tj`](https://wandb.ai/bojieli-pine-ai/adapt_think_verl/runs/wubbn5tj)
+- Baseline-only run: [`dblyx7cm`](https://wandb.ai/bojieli-pine-ai/adapt_think_verl/runs/dblyx7cm)
+- W&B project: [`bojieli-pine-ai/adapt_think_verl`](https://wandb.ai/bojieli-pine-ai/adapt_think_verl)
+
+The main run contains 411 training-history rows for steps 0–410 and 42
+validation rows at step 0 and every 10 steps through step 410. The baseline run
+contains the same step-0 validation metrics as the main run.
+
+## Training configuration
+
+| Item | Recorded value |
+| --- | --- |
+| Base model | `deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B` |
+| Historical source commit | `9e588202ff56fe93cdbe49f5594cf895f7d6b7c2` |
+| Hardware | 8 × NVIDIA H100 80GB HBM3 |
+| Runtime environment | CUDA 12.6, Python 3.13.7 |
+| Training data | DeepScaler |
+| Batch size | 128 |
+| Rollouts per prompt | 16 |
+| Prompt / response limit | 1,024 / 16,384 tokens |
+| NoThinking response limit | 4,096 tokens |
+| Learning rate | `2e-6` |
+| NoThinking bonus δ | 0.05 |
+| Save / validation interval | Every 10 steps |
+| Configured schedule | 10 epochs, 3,140 optimizer steps |
+| Selected report point | Step 300, approximately 28.37 hours |
+| Last retained point | Step 410, approximately 36.92 hours |
+| Final W&B state | `crashed` |
+
+The run therefore did not finish its configured ten-epoch schedule. The crash
+occurred after the selected step-300 report point.
+
+## Step-300 result
+
+The book uses step 300 as the comparison point. Accuracy and response length are
+the aggregate validation metrics logged by the main W&B run.
+
+| Dataset | Accuracy, step 0 | Accuracy, step 300 | Change | Mean response length, step 0 | Mean response length, step 300 | Reduction | NoThinking at step 300 |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| GSM8K | 0.79681577 | 0.81880212 | +2.1986 pp | 1,025.2350 | 477.3275 | 53.44% | 84.15% |
+| MATH500 | 0.81000000 | 0.81800000 | +0.8000 pp | 4,911.4600 | 1,576.6220 | 67.90% | 83.80% |
+| AIME2024 mean@16 | 0.31458333 | 0.31041667 | -0.4167 pp | 12,119.5063 | 6,402.2271 | 47.17% | 56.25% |
+
+Mean response length fell substantially on all three datasets. Accuracy improved
+on GSM8K and MATH500 but declined slightly on AIME2024, so this run does not
+support a claim of uniform accuracy improvement.
+
+### Conditional step-300 aggregates
+
+| Dataset | NoThinking accuracy | Thinking accuracy | NoThinking response length | Thinking response length |
+| --- | ---: | ---: | ---: | ---: |
+| GSM8K | 0.81621622 | 0.83253589 | 359.6414 | 1,102.3589 |
+| MATH500 | 0.82338902 | 0.79012346 | 1,089.7446 | 4,095.1605 |
+| AIME2024 mean@16 | 0.28680561 | 0.40963620 | 4,392.5965 | 8,927.4171 |
+
+The lower NoThinking rate on AIME2024 is consistent with difficulty-sensitive
+routing at the dataset level. Aggregate metrics do not prove that the model chose
+the correct mode for every individual problem.
+
+## Later retained telemetry
+
+Step 410 is shown separately because it is not the book's selected checkpoint.
+
+| Dataset | Accuracy at step 410 | Mean response length | NoThinking ratio |
+| --- | ---: | ---: | ---: |
+| GSM8K | 0.818044 | 464.56 | 82.03% |
+| MATH500 | 0.852000 | 1,481.91 | 74.80% |
+| AIME2024 mean@16 | 0.318750 | 5,873.74 | 49.79% |
+
+## Evaluation protocol represented by the logs
+
+- Maximum response length: 16,384 tokens.
+- Sampling temperature: 0.6; top-p: 0.95.
+- GSM8K and MATH500 use one sampled response per problem.
+- AIME2024 uses 16 sampled responses per problem and reports mean@16.
+- Answers are graded using the project's boxed-answer rule-based grader.
+
+These are in-training validation metrics. They are not results from a separately
+retained post-conversion evaluation run.
+
+## Checkpoint and provenance boundary
+
+The step-300 history includes a checkpoint-save timing event, but the checkpoint
+is not distributed with the book. There is also no public receipt showing that
+this historical checkpoint was converted and evaluated by `run_eval_verl_hf.sh`,
+and no retained MMLU rerun.
+
+The W&B main run records source commit
+`9e588202ff56fe93cdbe49f5594cf895f7d6b7c2`. The repository's future
+reproduction instructions pin its direct child
+`0033ad172dd53ac64004b763477407014f21b838`; the preprocessing, training, and
+evaluation entrypoints are unchanged between those commits.
+
+One manual correction is required for a future train-to-evaluate run. The
+training script interpolates an undefined `adapt_think_max_response_length` into
+the experiment name, producing a `-fl-` path segment. The evaluation script
+instead expects `-fl4096` and a different checkpoint directory layout.
+
+## Limitations
+
+- This is one historical run, not a multi-seed replication.
+- No per-example step-300 predictions, RNG state, or complete main-run stdout was
+  retained.
+- GSM8K and MATH500 use stochastic single-sample validation.
+- No confidence intervals or statistical-significance claims are provided.
+- Checkpoint selection and reporting use the same validation suites.
+- The results support a descriptive account of the logged run, not a causal or
+  universal claim about difficulty awareness.
+
+Within those boundaries, Experiment 8-10 is complete as a checkpoint-free
+training report.