The paper introduces T‑Router, a parameter‑efficient reinforcement learning framework that learns thalamic routing to improve reasoning with a pretrained model. T‑Router focuses adaptation on reusing completed computations via a compressed, addressable bank and a depth‑recurrent controller, allocating only 0.466% of the backbone parameters while achieving higher MathAvg scores on GSM8K and improving AIME accuracy. The study demonstrates that controlled computation reuse can outperform full‑parameter and LoRA approaches in reasoning tasks.
arXiv:2610.16489v1 Announce Type: cross Abstract: Reinforcement learning (RL) can substantially improve the reasoning capabilities of large language models, but an effective training run requires con...
Route-and-Generate (Rouge) is a hierarchical training framework that lets large language models decide how much reasoning to apply to each input. It separates reasoning effort from answer generation by using a routing policy that distributes a fixed sampling budget across different reasoning formats, while the base model is trained with GRPO and per-sample rewards. The method employs a rank‑based reward favoring shorter correct solutions and updates the router through a closed‑form KL‑regularized policy improvement, leading to more concise answers on easy tasks and longer reasoning on harder ones, thereby improving accuracy‑efficiency trade‑offs on multiple reasoning benchmarks.
CounterRoute is an online reinforcement‑learning framework that jointly learns how to route a language model’s reasoning between a ‘think’ and a ‘direct answer’ mode, using a single shared policy derived from a dual‑mode checkpoint. It employs counterfactual rollouts to credit routing decisions and a curriculum that starts with forced dual‑mode rollouts before shifting to self‑routed updates, achieving better accuracy‑efficiency trade‑offs across nine benchmarks. The method reduces generated tokens by up to 51% on Qwen3‑8B while improving macro‑average accuracy, and its routing strategy generalizes to unseen coding, science, and commonsense tasks.
arXiv:2606. 00395v1 Announce Type: cross Abstract: Mixture of Experts (MoE) Large Language Models (LLMs) achieve strong performance at scale.
arXiv:2609.13058v1 Announce Type: new Abstract: Reinforcement learning (RL) has become central to post-training of large language models. Recent advances in RL for Mixture-of-Experts (MoE) models hav...
arXiv:2608. 12146v1 Announce Type: cross Abstract: Training Mixture-of-Experts (MoE) models for reinforcement learning (RL) couples two load-balancing problems: sequence composition determines dense attention work in each data-parallel microbatch, while token routing determines sparse expert work on expert-parallel ranks.
arXiv:2602. 17993v2 Announce Type: replace-cross Abstract: Complex problems, whether in math, logic, or planning, are solved by humans through a sequence of steps where the result of one step informs the next.
When2Think introduces a post‑training framework that dynamically allocates reasoning depth in Large Reasoning Models based on instance difficulty. The method uses Instance‑level Difficulty‑Aware Control (IDAC) to shape rewards with pre‑computed accuracy and token usage statistics, enabling stable, critic‑free optimization without learned reward models. Experiments on mathematical benchmarks show that When2Think improves accuracy‑efficiency trade‑offs, achieving higher Pass@3 scores while reducing token usage compared to baseline models.
arXiv:2610.06647v2 Announce Type: replace Abstract: Reinforcement learning has greatly advanced the capabilities of large language models, but its memory demands remain a barrier to broader adoption....
arXiv:2606. 17803v1 Announce Type: new Abstract: Large language models achieve strong reasoning performance by scaling inference-time compute, yet remain fundamentally stateless, discarding the rich, self-produced reasoning traces generated during this process.
arXiv:2609.08189v1 Announce Type: new Abstract: Dynamic layer routing reduces the inference cost of Large Language Models (LLMs) by learning to skip layers for individual tokens. Existing methods, ho...
arXiv:2609.37304v1 Announce Type: new Abstract: Large reasoning models improve performance on challenging problems by allocating additional computation before answering, but longer reasoning does not...