Does RL Incentivize Reasoning in LLMs Beyond the Base Model?

>Our key finding is that all reasoning paths in the RLVR model are already present in the base model.

This is a really good observation. It means that you don't need to RL the full model. You merely need to RL a few LoRAs or maybe a small Mamba model appended to the final layer.

> If you run a regular models chat template with a reasoning tuned model, it can go back to acting like the base model, with no "thinking" process.

Well, of course. They've been "fine-tuned" with specific chat templates. Remove those and the fine-tune doesn't take precedence anymore. That's expected behaviour I'd say.

> "Reasoning" models are not any better than non reasoning models. It's a parlor trick, and benchmarks which claimed it wasn't are bad.

All of them? Including the closed ones, never public? I highly doubt that.

At least with Deep Seek math (with the same RL technique as the later R1) they noted similar things in their paper in the "Why RL Works?" section. Around the 1:04:00 mark of this Yannic Kilcher video review of the Deepseek math paper he goes over that section and points to basically the same limitations as the hn submission paper, starts at around the 1hr 4m mark and ends with this:

    1:05:40
    the Improvement is attributed to boosting the correct response from Top K
    1:05:46
    rather than the enhancement of fundamental capabilities this is something that we've come to learn in a
    1:05:52
    lot of different ways from like reinforcement learning on language
    1:05:58
    models or even supervised fine-tuning is that what's happening most likely is
    1:06:04
    more that the capabilities of doing all of these things are already present in
    1:06:09
    the underlying pre-trained language model

https://www.youtube.com/watch?v=bAWV_yrqx4w&t=1h4m

from the paper:

> 5.2.2. Why RL Works? > In this paper, we conduct reinforcement learning based on a subset of instruction tuning data, and it achieves significant performance enhancement upon the instruction tuning model. > To further explain why reinforcement learning works. We evaluate the Pass@K and Maj@K accuracy of the Instruct and RL models on two benchmarks. As shown in Figure 7, RL enhances Maj@K’s performance but not Pass@K. These findings indicate that RL enhances the model’s overall performance by rendering the output distribution more robust, in other words, it seems > that the improvement is attributed to boosting the correct response from TopK rather than the enhancement of fundamental capabilities. Similarly, (Wang et al., 2023a) identified a misalignment problem in reasoning tasks within the SFT model, showing that the reasoning performance of SFT models can be improved through a series of preference alignment strategies > (Song et al., 2023; Wang et al., 2023a; Yuan et al., 2023b).

In the video he reads into this that these methods alone may not at all get us over the data wall and are still fundamentally limited by the distribution of the base model they augment.

Thanks for sharing. I had trouble reading the transcript, so here is Claude's cleaned up version and summary:

Here's the condensed and formatted transcription in a single paragraph: This is the last thing I want to highlight this section on why RL works. Here they evaluate different things - they evaluate specifically pass at K and maj at K. Maj at K is like majority voting, so what you do is you have a model, you have a question, and you output not just one output but an ordered set. So you give your top 20 answers - 0 is your best answer that the model wants to give most, then the second most answer, third most answer, and so on. They could all be correct, just different reformulations of the same answer or different derivations stated in different ways. What you're interested in is how many of the top K results are correct - that's the pass at K. And if you had to vote if majority voting on the top K, how often would you be correct then? There's a slight difference, and that slight difference is actually made more drastic by reinforcement learning. They say, "As shown in figure 7, reinforcement learning enhances majority at K performance but not pass at K." These findings indicate that reinforcement learning enhances the model's overall performance by rendering the output distribution more robust. In other words, it seems that the improvement is attributed to boosting the correct response from Top K rather than the enhancement of fundamental capabilities. This is something we've come to learn in many different ways from reinforcement learning on language models or even supervised fine-tuning - what's happening most likely is that the capabilities of doing all of these things are already present in the underlying pre-trained language model. Summary: Reinforcement learning improves language model performance not by enhancing fundamental capabilities but by making the output distribution more robust, effectively boosting correct responses within the top results rather than improving the model's inherent abilities.

This is an interesting scenario: do you know of any documented examples?