Paper

Batched Stochastic Linear Bandits with 1-Bit Communication Constraints

Unreadunread

AI summary

arXiv:2605. 30976v1 Announce Type: new Abstract: We study stochastic linear bandits under a natural combination of batching and communication constraints: the time horizon is partitioned into batches of equal size $B$, and during each batch the learner sends $B$ requested arm pulls to an agent, who then observes the corresponding $B$ rewards and responds with a single bit of feedback to the learner.

Reading status

Priority

About this paper

Authors: Ivan Lau, Daniel McMorrow, Kevin Jamieson, Jonathan Scarlett
Released: 2026-06-01 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Batched Stochastic Linear Bandits with 1-Bit Communication Constraints overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: rect, under, middle, line, rate, trained, factor, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.30976

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Batched Stochastic Linear Bandits with 1-Bit Communication Constraints. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.