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In the g factor benchmark reported on Oct. 6, 2026, DAPO led the no-think holdout with a native score of 0.6467 (39/40 successful episodes), while DAPO-refill led in Thinking-2048 with 0.7105 (38/40). But the untrained thinking-enabled model scored 0.6830, ahead of every trained thinking configuration except DAPO-refill. The results point to different leaders by reasoning mode—not a universal best trainer.
What did the benchmark test?
The benchmark article evaluated Qwen3-14B on a deterministic decentralized-exchange (DEX) arbitrage task. Each agent received a frozen snapshot of automated market-maker pools and had to find an arbitrage cycle involving two to four pools, then choose an input amount that remained profitable after fees, gas and slippage. A simulated EVM verifier checked whether the proposed execution worked.
There were two reasoning conditions: no-think and Thinking-2048. The frozen holdout used five task families, two held-out seeds and four samples per task: 40 episodes across 10 distinct unseen scenarios. The article evaluated final step-50 checkpoints and also ran the untrained model in both reasoning modes.
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#1 Best Overall
How did the configurations score on the holdout?
The following are native holdout scores and successful episodes reported by the g factor benchmark article on Oct. 6, 2026. They are not the composite training-reward values.
| Configuration | No-think native holdout | Thinking-2048 native holdout |
|---|---|---|
| Untrained baseline | 0.3856 (16/40) | 0.6830 (37/40) |
| GRPO | 0.6424 (38/40) | 0.6798 (38/40) |
| DAPO | 0.6467 (39/40) | 0.6703 (38/40) |
| DAPO-refill | 0.5839 (34/40) | 0.7105 (38/40) |
| GDPO | 0.6245 (36/40) | 0.6602 (35/40) |
| CISPO | 0.6190 (36/40) | 0.6068 (33/40) |
| DAPO + ADAPO | 0.6332 (37/40) | 0.6250 (34/40) |
| DAPO + REPO-R | 0.5724 (31/40) | 0.6461 (34/40) |
| CISPO + REPO-R w5 | not reported by the benchmark article | 0.6243 (34/40) |
What do the no-think and thinking results tell us?
No-think: DAPO had the highest reported score
DAPO recorded the top no-think native holdout score, 0.6467, with 39 of 40 episodes successful. GRPO was close at 0.6424, with 38 successes. Because the holdout spans only 40 episodes and 10 scenarios, this small score difference is a result for this benchmark, not strong evidence that DAPO will outperform GRPO on other tasks.
Rank #2
Thinking-2048: DAPO-refill led, but the untrained baseline was competitive
DAPO-refill reached 0.7105 (38/40), the highest reported Thinking-2048 result. The untrained thinking-enabled baseline scored 0.6830 (37/40), higher than the other trained configurations in that condition. GRPO and DAPO each had 38 successful episodes, yet their scores—0.6798 and 0.6703—were below the baseline. Success count alone therefore does not show which configuration produced the stronger native holdout score.
Training reward did not reliably predict holdout performance
The article defines composite training reward as native task score plus 0.05 times execution-efficiency reward plus 0.20 times reliability reward. Holdout results report native score only, so the two measures answer different questions. In Thinking-2048, CISPO’s last-10 training reward mean was 0.8951, while its native holdout score was 0.6068. That gap illustrates why a rising training curve should not be treated as proof of better performance on unseen tasks.
What distinguishes the algorithms?
- GRPO (Group Relative Policy Optimization): Introduced in DeepSeekMath as a PPO variant, it uses group scores to estimate a baseline rather than a critic model, reducing memory requirements.
- DAPO (Decoupled Clip and Dynamic Sampling Policy Optimization): The DAPO project describes techniques including Clip-Higher, dynamic sampling, token-level policy-gradient loss and overlong-reward shaping.
- CISPO (Clipped Importance Sampling Policy Optimization): NVIDIA’s NeMo-RL documentation describes clipping importance-sampling weights as detached coefficients rather than applying PPO-style hard ratio clipping.
- GDPO (Group reward-Decoupled Normalization Policy Optimization): Its paper describes decoupled normalization for multi-reward optimization.
DAPO-refill, ADAPO, REPO-R and the combinations in the table are the benchmark author’s tested configurations. Their results should not be assumed to describe every implementation using those method names.
How much more costly was the thinking condition?
On the benchmark’s reported hardware and setup, Thinking-2048 steps took 4.5–5.9 times longer than no-think steps. The article reports 12.3–14.1 GPU-hours for 50 thinking-mode steps, versus 2.2–3.1 GPU-hours for 50 no-think steps. Peak allocated GPU memory for the listed thinking-mode runs was 66.5–71.8 GiB. These figures describe this experiment, not general training-cost estimates.
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The frozen holdout and untrained baselines make this comparison more informative than training curves alone, but the evidence remains narrow. It comes from one task environment, one reported model setup and a holdout of 40 episodes across 10 scenarios. The benchmark article does not establish confidence intervals or repeated independent training runs, so close score differences should not be read as statistically established rankings. The practical conclusion is to evaluate the intended reasoning mode and unseen-task performance directly rather than selecting an algorithm from its name or training reward alone.
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