Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
On the Power of Context-Enhanced Learning in LLMs
Authors: Xingyu Zhu, Abhishek Panigrahi, Sanjeev Arora
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using a multi-step reasoning task, we prove in a simplified setting that context-enhanced learning can be exponentially more sample-efficient than standard learning when the model is capable of ICL. At a mechanistic level, we find that the benefit of context-enhancement arises from a more accurate gradient learning signal. We also experimentally demonstrate that it appears hard to detect or recover learning materials that were used in the context during training. This may have implications for data security as well as copyright. ... Section 3 details our experiments and the findings sketched above. ... Figure 2 demonstrates the significant sample efficiency of context-enhanced learning. |
| Researcher Affiliation | Academia | Xingyu Zhu 1 * Abhishek Panigrahi 1 * Sanjeev Arora 1 1Princeton Language and Intelligence, Princeton University. Correspondence to: <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Context-Enhanced Learning In contrast to standard SFT, it relies on curriculum-text in context on which no auto-regressive loss is computed. ... Algorithm 2 Context-Enhanced Searching Algorithm for MLT(d, n) ... Algorithm 3 Context-Enhanced Layerwise Gradient Descent ... Algorithm 4 Layerwise Gradient Descent with Context-Enhanced Learning For Optimizing SURR-MLT ... Algorithm 5 Full Parameter Gradient Descent with Context-Enhanced Learning For Optimizing SURR-MLT |
| Open Source Code | No | The paper does not provide explicit statements about releasing code or links to a code repository for the methodology described. |
| Open Datasets | No | Supervised dataset DΠ is curated with input-label pairs of the form (s1, [<THINK>,..., MLTΠ (s1)]), where s1 is a random string sampled from A1, with length between 20 and 40. ... Our experiments focus on a synthetic MLT task for a few reasons: (1) to ensure that the task is absent from LLM pre-training, which allows precise quantification of benefits of context-enhanced learning, including not revealing the curriculum text at inference time. |
| Dataset Splits | No | We construct supervised datasets DΠ with 10^4 to 10^6 unique samples and train the models for one epoch on each. ... We report the next-token prediction accuracy on the final answer tokens (ignoring thought tokens) for held-out samples when conditioning on no curriculum-text (100% dropped-out) and compare against the supervised dataset size. |
| Hardware Specification | No | The paper mentions using "Llama 3.2-3B instruction-tuned model" but does not provide any specific details about the hardware (e.g., GPU, CPU models) used for training or experiments. |
| Software Dependencies | No | We use Adam W optimizer (Loshchilov & Hutter, 2019) with weight decay fixed at 10^-4. We use cosine learning rate schedule (Loshchilov & Hutter, 2016), with peak learning rate 10^-4 and a 6% warmup phase, where learning rate is linearly increased from 0 to the peak. |
| Experiment Setup | Yes | We use the Llama 3.2-3B instruction-tuned model (Dubey et al., 2024) as the base model and fix d = 5 with n = 8 or 10. ... Training hyperparameters are set equal to the optimization hyperparameters used in preparation of ICL-capable training phase (Appendix B.4), except we set weight decay to 0 in all experiments. ... We use cosine learning rate schedule (Loshchilov & Hutter, 2016), with peak learning rate 10^-4 and a 6% warmup phase, where learning rate is linearly increased from 0 to the peak. We use Adam W optimizer (Loshchilov & Hutter, 2019) with weight decay fixed at 10^-4. We use a batch size of 64 for training. ... For the first 10% fraction of training, we train the model with explicit Co T tokens... Then between 10% to 60% fractions of training, Co T tokens are gradually replaced by <THINK> tokens... After that, the model is trained with the <THINK> Co T tokens till the end of training. |