Viewing enriched Twitter post
Confused about recent LLM RL results where models improve without any ground-truth signal? We were too. Until we looked at the reported numbers of the Pre-RL models and realized they were serverely underreported across papers. We compiled discrepancies in a blog below🧵👇 https://t.co/Hmn41grrrh
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"full_text": "@Grad62304977 @rm_rafailov @Swarooprm7 @j_foerst @kimmonismus @iScienceLuvr @Teknium1 @aryaman2020 @khoomeik @_akhaliq Yes agreed, I personally really like the verifree method as I had independently been thinking about this for the past few months! That makes most sense in all the papers we discuss.\n\nStill, reporting incorrect reference numbers can mislead readers, so it fits the broader theme",
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"text": "I agree that having a consistent evaluation pipeline and better illustrating format and non-format gain are important, we also update the format reward baseline in 1-shot RLVR (https://t.co/Td0qzKGWjv) to more fairly report non-format gain (and we see there is still nontrivial non-format gain). But I disagree with some points in the blog for 1-shot RLVR.\n\n1. For Deepseek-R1-Distill-Qwen-1.5B, we set max generation length to be 8192 tokens for both before and after RL models, but the values you compare with us using 32768 tokens, which is not mentioned in your report. This affects a lot because R1-Distill is a long-CoT model since some responses will be truncated. It's very slow to train with 32768 token length, so we follow DeepScaleR's first stage to train with 8192 context width (which is still slow actually) and evaluate at the same token length. It's more likely to do reasoning compression here (as DeepScaleR stage 1), and we don't claim that 1(few)-shot RLVR is doing reasoning capability improvement for R1-Distill exps here, just say it can be close with RLVR with 1.2k data.\n\n2. We use the official Qwen Evaluation pipeline and the initial values of MATH500 on Qwen families in our paper are close to that in the official Qwen2.5(-Math) official report (although they report 4-shot results. We also test 4-shot results and they are better than 0-shot). e.g., (ours' 0-shot vs ours' 4-shot vs Qwen reports' 4-shot): 7B: 51.0 vs 59.2 vs 55.4; 1.5B: 36.0 vs 49.8 vs 49.8. Although using other template or non-template (https://t.co/XTMBR9AbcK) will be better, they are still reasonable values as we consistently use the same evaluation pipeline\n\n3. Consistently using the same evaluation before and after RL already provides fair comparison, no matter format or non-format gain. We also claim the comparison between 1-shot RLVR and RLVR with 1.2k (7.5k) data to prevent absolute bias caused by evaluation. All the evaluation details are mentioned in the paper, and people can access our code, model checkpoint, data for reproduction: https://t.co/ao8ci5n66p. Different papers may use different templates, temperatures, which may have significantly different performance (especially on Qwen), but this is orthogonal to the findings.\n\n4. I think how to accurately calculate format gain is not easy. For RLVR I think format reward will be a good baseline (as we update above). In-context learning is not stable, and it may have mixed format and non-format gain, and can be optimized. \n\nWe recently find that using pi1 (the 1-shot data used in our paper) with intermediate model response for 1-shot in-context learning is already capable to get 75% MATH500 and 41.3% OlympiadBench score on Qwen2.5-Math-7B without any training (although not work for all models). I think it's better than other non-trained templates and settings (as 64.3 used in your report) and actually be close to the RLVR result. The evaluation settings may be optimized but it will be relatively hard to retrain all the models once there are some better settings.\n\nIn general, I think these papers actually convey the same message that base models already have strong performance, and I believe that can be achieved by more easily non-RL methods. But the true effectiveness of RL should be the marginal improvement.",
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"is_reader_mode_available": true
},
"scribeConfig": {
"page": "ranked_replies"
}
}
}
}
}