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GuidedSampling: Steering LLMs Towards Diverse Candidate Solutions at Inference-Time |
Soundness: 2: fair
Presentation: 2: fair
Contribution: 3: good
Rating: 4: marginally below the acceptance threshold
Confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. |
The paper introduces GuidedSampling, a straightforward and effective method that proposes high-level concepts for the LLM to follow before generating specific solutions. Experiments see performance increase as well as response variety increase with large pass@K sampling.
1. By intuition, there is almost no doubt in my mind that the proposed method is effective, as it resembles a mini-agentic approach and directly scales up reasoning through segmentation of hard problems. It's great to see this type of approach being studied in the field, however simple they may be.
2. The empirical justifications are well-discussed, and the results are significant and generalizable.
1. The theoretical Section 3.3 leaves much to be desired.
1. Assumption 1 (minor issue: notation $C_{inf}$ should be $C_r$) has four lines of equations, but the second and third are equivalent and both can be derived from the fourth. I suspect the authors wanted to justify the fourth assumption and introduction of $k_c$, but this is not a good way to write assumptions. Furthermore, the fourth line can be relaxed from an equation to a greater-than-inequality, which immediately relaxes the assumption to be much more reasonable, since the original version stipulates two partial probability distributions have the exact same ratio across $x$ and $y^\*$.
2. Theorem 1 is misleading for using "iff" on Line 281/282: Equation (2) is not equivalent to $P_{GS} > P_{RS}$, but can only derive the latter. This means it is possible that Equation (2) does not hold, but the desired advantage of GS over RS still holds. Despite this, I still think there is value in the theoretical analysis, since the passage under "Sufficient Concept Coverage" is still perfectly valid. I would suggest addressing these problems and rephrasing the theory to be more straightforward and clear.
2. The value of hyperparameter K, the number of concepts, is not clearly specified for the main result in Figure 4.
3. As a motivation, the diversity analysis in Figure 3 is a great way to motivate the method. It would be helpful to do more analyses on diversity of GuidedSampling without finetuning.
Q1. I'm not sure about the utility of fine-tuning on CCA synthetic data. A naive concatenation of concept + answer may shift the model rollout distribution to exhibit unprecedented behaviors (e.g. become more verbose, suggest unnecessary things), potentially decreasing model generalizability and utiliy. Do models fine-tuned on one synthetic dataset generalize its performance (not diversity) across others? What other validations did you do to ensure no model collapse occurs with CCA fine-tuning?
Q2. Do you have any results on whether the earlier concepts suggested by the concept generator are better than later ones, or vice versa? If there's a pattern there may be a better way to allocate resources than uniformly, but this is not very important so don't stress about it if results are hard to get. |
Fully human-written |
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GuidedSampling: Steering LLMs Towards Diverse Candidate Solutions at Inference-Time |
Soundness: 3: good
Presentation: 4: excellent
Contribution: 3: good
Rating: 8: accept, good paper
Confidence: 2: You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked. |
This paper provides a competitor approach to Repeated Sampling for the task of inference time algorithms to improve model performance on complex tasks. Their approach utilizes an exploration phase to sample relevant concepts first and then samples varying responses conditioned on such context to have a larger scope of responses at decision time. The work provides theoretical bounds for how it improves upon RS and additionally presents results on various benchmark complex reasoning datasets demonstrating improvements over baseline methods.
1. The problem of scaling inference time compute to generate better performance is interesting and well motivated especially in the context of smaller models.
2. The proposed approach is quite simple and easy to implement, yet very compelling and intuitive to the reader of why this would work well.
3. The theory provides crisp conditions by which GuidedSampling would be preferred to RS.
4. The empirical results are extensive and impressive.
1. The intuition for Assumption 1 is unclear. That is, if concepts are misapplied or conflicting, the assumption could easily be violated.
2. While the paper well established how candidate solution sets are constructed at inference time, it is still unclear how the final solution is selected from this set. Is it a majority vote?
3. All results are on models of ~3B size. To understand the scale of the solution, their should be more results for various model sizes.
1. How is the final response selection chosen from the candidate set?
2. How can we ensure that the concepts samples are semantically diverse? If they are not diverse enough, could we ever get worse performance than RS?
3. How sensitive are the results to prompt changes for concept generation? |
Fully human-written |
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GuidedSampling: Steering LLMs Towards Diverse Candidate Solutions at Inference-Time |
Soundness: 3: good
Presentation: 3: good
Contribution: 3: good
Rating: 6: marginally above the acceptance threshold
Confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked. |
The paper proposes GuidedSampling, a novel inference-time algorithm designed to address the limited diversity of solutions generated by standard Repeated Sampling (RS). The core idea is to decouple inference into two phases:
1. Exploration: Generate a diverse set of high-level concepts (e.g., theorems, programming paradigms) relevant to solving the input problem.
2. Generation: For each concept, generate multiple candidate solutions conditioned on that concept.
The authors provide a theoretical analysis showing conditions under which GuidedSampling outperforms RS (Theorem 1). Empirically, they demonstrate clear pass@50 improvements over RS across four benchmarks. Additionally, they show that fine-tuning models on GuidedSampling-generated trajectories yields clear pass@5 gains and increases solution diversity.
1. While multi-path reasoning exists (e.g., ToT), GuidedSampling's two-phase design with iterative concept prompting shows better performance.
2. The paper is well-organized and easy-to-follow. It is supported with theoretical results.
1. The paper distinguishes itself from previous works via iterative concept prompting and theoretical analysis, but the high-level idea of separating idea generation from solution generation is not entirely unprecedented.
2. Theorem 1 assumes concepts amplify correctness probability (Assumption 1). However, irrelevant/misleading concepts could harm performance.
3. It seems necessary to design task-specific definitions of concepts, which limits generalization.
1. Compared to the Tree-of-Thought (ToT) algorithm, GuidedSampling incurs lower computational overhead. However, ToT—being more expensive—should theoretically yield better performance, especially since GuidedSampling's approach appears subsumable within ToT’s framework (e.g., treating each concept as a root thought). Why then does ToT underperform relative to GuidedSampling in the experiments? Moreover, given the difference in computational cost, a direct comparison of inference-time overhead (e.g., total LLM calls, latency, or FLOPs) between the two methods is essential to contextualize their trade-offs.
2. How would GuidedSampling adapt to non-STEM tasks (e.g., commonsense reasoning, dialogue)? What would “concepts” represent in such settings? |
Lightly AI-edited |
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GuidedSampling: Steering LLMs Towards Diverse Candidate Solutions at Inference-Time |
Soundness: 2: fair
Presentation: 3: good
Contribution: 3: good
Rating: 6: marginally above the acceptance threshold
Confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked. |
This paper proposes GUIDEDSAMPLING, a novel inference-time algorithm designed to address the lack of diversity in traditional Repeated Sampling (RS). GUIDEDSAMPLING resolves this limitation by decoupling the inference process into an exploration phase and a generation phase. It first iteratively generates a diverse set of high-level concepts or theorems in the exploration phase, and then generates candidate solutions for each concept in the generation phase. This method can also be used as a synthetic data generation method. Experimental results show that GUIDEDSAMPLING brings model performance improvements both as an inference-time algorithm and as a synthetic data generation method.
1. The core idea of this paper—decoupling the inference process into an exploration phase and a generation phase—is an intuitive, simple, and effective method that directly targets the weakness of RS's lack of diversity.
2. The paper provides a theoretical explanation for when this method works, which is consistent with experimental observations
3. In my opinion, the paper's greater contribution lies in its excellence as a synthetic data generation method. It guides the model to generate more diverse reasoning results on its own, and further allows the model to internalize diversity through training.
1. The exploration phase is an iterative process, meaning that generating the k-th concept requires waiting for the (k-1) concepts to finish generating. This implies that acquiring K concepts necessitates K serial calls. As an inference-time method, GUIDEDSAMPLING is more challenging to ensure real-time performance compared to RS when computational resources are sufficient.
2. The paper lacks quantifiable comparisons of computational efficiency.
3. The definition of "concepts" appears to be task-specific and manually designed. How can this approach be generalized to more open-ended tasks where concepts are less clearly defined (e.g., commonsense reasoning, long-form story generation)?
4. Although the paper shows that the model can sometimes produce correct solutions even if flawed concepts were generated earlier, a more robust approach would be to implement a feedback loop, allowing the generation phase to inform the exploration phase. This would allow generation efforts to be focused on more promising concepts, rather than wasting compute on flawed paths.
See weaknesses |
Fully human-written |